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Dissertations / Theses on the topic 'Spatial interpolation'
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Martin, Peter. "Spatial interpolation in other dimensions /." Connect to this title online, 2004. http://hdl.handle.net/1957/4063.
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Gholmi, Allan. "Evaluating spatial mapping using interpolation techniques." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139704.
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In this thesis, the inverse distance weighting, different kriging methods, ordinary least squares and two variants of the geographically weighted regression was used to evaluate the spatial mapping abilities on an observed dataset and a simulated dataset. The two datasets contain the same bioclimatic variable, near-surface air temperature, uniformly distributed over the whole world. The observed dataset is the observed temperature of a global atmospheric reanalysis produced by ECMWF and the other being simulated temperature produced by SMHI’s climate model EC-earth 3.1. The data, initially containing space-time information during the time period 1993-2010 displayed no significant temporal variation when using a spatio-temporal variogram. However, each year displayed its own variation so each year was split where the different methods were used on the observed dataset to estimate a surface for each year that was then used to make comparisons to the simulated data. CLARA clustering was done on the observed geographical dataset in the hope to force the inverse distance weighting and the kriging methods to estimate a locally varying mean. However, the variograms produced displayed an irregular trend that would lead to inaccurate kriging weights. Geometric anisotropy variogram analysis was accounted for that displayed moderate anisotropy. Results show that the geographically weighted regression family outperformed the rest of the used methods in terms of root mean squared error, mean absolute error and bias. It was able to create a surface that had a high resemblance to the observed data.
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Schmidt, Alexandra Mello. "Bayesian spatial interpolation of environmental monitoring stations." Thesis, University of Sheffield, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.370075.
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Gorsich, David John 1968. "Nonparametric modeling of dependencies for spatial interpolation." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/9029.
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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2000.Includes bibliographical references (p. 140-148).
Crucial in spatial interpolation of stochastic processes is the determination of the underlying dependency of the data. The dependency can be represented by an underlying covariogram, variogram, or generalized covariogram. Estimating this function in a nonparametric way is the theme of this thesis. If the function can be found accurately, then kriging is the optimal linear interpolation technique. A nev,· technique for variogram model selection using the derivative of the empirical variogram and non-negative least squares is discussed. The eigenstructure of the spatial design matrix, the key matrix in Matheron's variogram estimator is determined. Then a nonparametric estimator of the variogram and covariogram of a spatial stochastic process is found. The optimal node selection is determined as well as conditions when the spectral coefficients can be found without a non-linear algorithm. A method of extending isotropic positive definite functions in ]Rd is determined in order to avoid a Gibbs effect on the Fourier-Bessel expansion. Finally, a nonparametric estimator of the generalized covariance is discussed.
by David John Gorsich.
Ph.D.
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Cui, Haiyan. "Robustness and Bayesian analysis of spatial interpolation." Diss., The University of Arizona, 1995. http://hdl.handle.net/10150/187077.
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Kriging is a well known spatial interpolation technique widely used in earth science and environment sciences, the variogram plays a central role in the kriging predictor. In this dissertation, we will mainly study two problems which are closely related to the kriging predictor. The first one is how the variogram affects the kriging predictor and how this effect is qualified. The second one is how to approach kriging with an uncertain variogram, which includes both the functional form and the parameters in the variogram. For the first problem, some investigation of robustness of kriging predictor have been done by some authors. And for the second one, two frameworks have been used to approach the kriging with uncertain variogram in recent years. For the formal approach, the Bayesian framework is used to achieve the goal, and for the latter one, the fuzzy set theory is used, which mainly means that the kriging with an uncertain variogram is represented by the calculated membership function for each kriged value. The object of this dissertation is to extend the robustness results of kriging, to generalize the robustness concept to the cross-validation method, and to study the robustness of the cross-validation. We define the influence function of kriging and cross-validation technique and derive their influence functions in terms of perturbation of variogram and sample configuration. We will derive some different Bayesian kriging models under different assumptions and study their properties. We will also modify the fuzzy kriging model. Moreover we discuss the relationship between Bayesian kriging and fuzzy kriging and relate the fuzzy kriging to the robustness of kriging. Finally, in this work, we will show the power of Bayesian kriging and display its advantage as an interpolation technique for the analysis of spatial data. This is done through the presentation of a case study.
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Davies, Helen Catherine. "Bovine TB in badgers : a spatial analysis." Thesis, University of Bristol, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.289778.
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Atalay-Satoglu, Fatma Betul. "Spatial decompositions for geometric interpolation and efficient rendering." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/1812.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2004.Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Höglund, Melker. "Machine Learning Methods for Spatial Interpolation of Wind." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275743.
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In this study, two popular machine learning approaches and a number of common simple spatial interpolation techniques are applied to spatial estimation of wind field observations in Sweden. Specifically, neural network and random forest models using geographical coordinates as input variables are considered. Furthermore, the addition of elevation as a secondary input is studied. The accuracy of the methods is assessed using a leave-one-out cross-validation scheme. Visual examination of the resulting interpolation fields and interpolation errors is used as an additional point of comparison. The results show that random forests with elevation included as a secondary input produces the smallest errors of all methods tested. It is thus concluded that it is possible to achieve greater accuracy using machine learning based models than simple traditional interpolation methods.Denna studie jämför två populära maskininlärningsmetoder samt ett antal vanliga enklare metoder för interpolation av vindfältsobservationer från Sverige. Specifikt betraktas neurala nätverk och random forests, med huvudsakligen geografiska koordinater som indata. Vidare studeras även dessa modeller med höjd över havet av observationerna som ytterligare indata. Noggrannheten av metoderna undersöks med hjälp av leave-one-out-korsvalidering. Interpolationsresultaten samt interpolationsfelen studeras även visuellt som ytterligare jämförelsepunkt. Resultaten visar att random forests med höjddata inkluderad producerar de minsta felen av alla testade metoder. Från detta dras slutsatsen att det är möjligt att uppnå bättre noggrannhet med interpolationsmetoder baserade på maskininlärning jämfört med traditionella metoder.
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McNeill, Lindsay. "Topics in interpolation and smoothing of spatial data." Doctoral thesis, University of Cape Town, 1994. http://hdl.handle.net/11427/15969.
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Bibliography: p. 176-187.This thesis addresses a number of special topics in spatial interpolation and smoothing. The motivation for the thesis comes from two projects, one being to extend the availability of a daily rainfall model for southern Africa to sites at which little or no rainfall data is available, using data from nearby sites, and the other arising from a need to improve the species abundance estimates used to produce maps for the Southern African Bird Atlas Project in areas where the original presence/absence data is sparse. Although problems of spatial interpolation and smoothing have been the subject of much research in recent years, leading to the development of the specialised discipline of geostatistics, these two problems have features which render the available methodology inappropriate in certain respects. The semi-variogram plays a central role in geostatistical work. In both of the applications considered here, the raw semi-variogram is 'contaminated' by error, but the error variance varies widely between data points, so that the spatial autocorrelation structure of the underlying error-free variable is blurred. An adjusted semi-variogram, which removes the effect of the measurement error, is defined and incorporated into the kriging equations. A number of measures have been proposed for kriging in the presence of trend, ranging from explicit modelling of a deterministic trend function to 'moving window' kriging, which assumes local stationarity as an approximation. The former approach is often inappropriate over large non-homogenous regions, while the latter approach tends to underestimate the kriging variance. As an alternative strategy it is proposed here that the trend function be considered as another random variable, with a long-range spatial autocorrelation. This approach is simple to implement, and can also be used as a basis for filtering the data to separate trend from local or high-frequency variation. The daily rainfall model is based on a Fourier series representation giving rise to amplitude and phase parameters; the latter are circular in nature, and not amenable to analysis by standard techniques. This thesis describes a method of interpolation and smoothing, analogous to kriging, which is appropriate for unit vector data available at a number of spatial locations. The cumulated values of species counts in the SABAP are essentially binomially distributed and thus again specialised techniques are required for interpolation. New geostatistical methods which cater for both binomial and Poisson data are presented. Another problem arises from the need to improve interpolated values of the rainfall model parameters by incorporating information on altitude. Although a number of approaches are possible, for example, using co-kriging or kriging with external drift, difficulties are caused by the complexity of the relationship between the rainfall at a point and the surrounding topography. This problem is overcome by the use of orthogonal functions of altitude to model the patterns of topography.
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Khosravan, Najafabadi Shohreh. "Optimal vector interpolation of asynoptic spatial survey of vector quantities for interpolating ADCP water velocity measurements." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27381.
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In fields of study such as geophysics and hydraulics, many random variables are vector quantities, not scalars. Vector quantities require statistical techniques that are independent of choice of coordinate system. In this research a new optimal vector interpolation method, suitable for interpolation of asynoptically measured spatial vector fields, was developed and tested. The new method was compared to scalar interpolation by kriging. The test data were spatial Acoustic Doppler Current Profiler (ADCP) surveys of depth average fluvial water velocity in reaches upstream and downstream of a bridge. The interpolation procedures were evaluated by interpolating the fields with various amounts of data removal, and comparing to the actual measured field using a vector correlation coefficient previously developed by Crosby et al. (1993). The new optimal vector interpolation method was superior to kriging when all data were utilized (upstream reach) and for data removal rates of up to 30% (downstream reach).
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Zhang, Qingping. "Intelligent computing for 2D spatial information interpolation in GIS." Thesis, City University London, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522912.
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Collins, Fred C. "A comparison of spatial interpolation techniques in temperature estimation." Diss., This resource online, 1995. http://scholar.lib.vt.edu/theses/available/etd-06062008-155740/.
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Asratyan, Albert. "Performance Analysis of Distributed Spatial Interpolation for Air Quality Data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296339.
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Deteriorating air quality is a growing concern that has been linked to many health- related issues. Its monitoring is a good first step to understanding the problem. However, it is not always possible to collect air quality data from every location. Various data interpolation techniques are used to assist with populating sparse maps with more context, but many of these algorithms are computationally expensive. This work presents a three- step chain mail algorithm that uses kriging (without any modifications to the kriging algorithm itself) and achieves up to ×100 execution time improvement with minimal accuracy loss (relative RMSE of 3%) by parallelizing the load for the locally tested data sets. This approach can be described as a multiple- step parallel interpolation algorithm that includes specific regional border data manipulation for achieving greater accuracy. It does so by interpolating geographically defined data chunks in parallel and sharing the results with their neighboring nodes to provide context and compensate for lack of knowledge of the surrounding areas. Combined with the cloud serverless function architecture, this approach opens doors to interpolating data sets of huge sizes in a matter of minutes while remaining cost- efficient. The effectiveness of the three- step chain mail approach depends on the equal point distribution among all regions and the resolution of the parallel configuration, but in general, it offers a good balance between execution speed and accuracy.Försämrad luftkvalitet är en växande oro som har kopplats till många hälsorelaterade frågor. Övervakningen är ett bra första steg för att förstå problemet. Det är dock inte alltid möjligt att samla in luftkvalitetsdata från alla platser. Olika interpolationsmetoder används för att hjälpa till att fylla i glesa kartor med mer sammanhang, men många av dessa algoritmer är beräkningsdyra. Detta arbete presenterar en trestegs ‘kedjepostalgoritm’ som använder kriging (utan några modifieringar av själva krigingsalgoritmen) och uppnår upp till × 100 förbättring av exekveringstiden med minimal noggrannhetsförlust (relativ RMSE på 3%) genom att parallellisera exekveringen för de lokalt testade datamängderna. Detta tillvägagångssätt kan beskrivas som en flerstegs parallell interpoleringsalgoritm som inkluderar regional specifik gränsdatamanipulation för att uppnå större noggrannhet. Det görs genom att interpolera geografiskt definierade databitar parallellt och dela resultaten med sina angränsande noder för att ge sammanhang och kompensera för bristande kunskap om de omgivande områdena. I kombination med den molnserverfria funktionsarkitekturen öppnar detta tillvägagångssätt dörrar till interpolering av datamängder av stora storlekar på några minuter samtidigt som det förblir kostnadseffektivt. Effektiviteten i kedjepostalgorithmen i tre steg beror på lika punktfördelning mellan alla regioner och upplösningen av den parallella konfigurationen, men i allmänhet erbjuder den en bra balans mellan exekveringshastighet och noggrannhet.
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Paulionienė, Laura. "Statistical modelling of spatio-temporal data based on spatial interpolation of time series parameters." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2013~D_20140117_113114-31261.
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Space – time data modeling problem is analysed. Often spatial data sets are relatively small, and the points, where observations are taken, are located irregularly. When solving spatial task, usually we are interpolating or estimating the spatial average. Time series data usually are used to predict future values. Meanwhile, the space - time tasks combines both types of tasks. Few original modeling methods of spatial time series are proposed. The proposed methods firstly analyzes the univariate time series, and after removing temporal dependence, spatial dependence in the time series of residuals is measured. Aim of this dissertational work - to create time series model at new unobserved location by incorporating spatial interaction thru spatial interpolation of estimated time series parameters. Such a model is based on the spatial interpolation of time series parameters.Disertaciniame darbe nagrinėjama erdvės – laiko duomenų modeliavimo problema. Dažnai erdvinių duomenų rinkiniai yra gana nedideli, o taškai, kuriuose pasklidę stebėjimai, išsidėstę netaisyklingai. Sprendžiant „erdvinį“ uždavinį, paprastai siekiama inerpoliuoti arba įvertinti erdvinį vidurkį. Laiko eilučių duomenys dažniausiai naudojami ateities reikšmėms prognozuoti. Tuo tarpu erdvės – laiko uždaviniai jungia abu uždavinių tipus. Pasiūlyta keletas originalių erdvinių laiko eilučių modeliavimo metodų. Siūlomi metodai pirmiausia analizuoja vienmates laiko eilutes, o pašalinus laikinę priklausomybė jose, laiko eilučių liekanoms vertinama erdvinė priklausomybė. Tikslas – sudaryti modelį, leidžiantį prognozuoti požymio reikšmę naujame, nestebėtame taške, nauju laiko momentu. Tokio modelio sudarymas remiasi laiko eilučių parametrų erdviniu interpoliavimu.
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Memarsadeghi, Nargess. "Efficient algorithms for clustering and interpolation of large spatial data sets." College Park, Md. : University of Maryland, 2007. http://hdl.handle.net/1903/6839.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2007.Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Soares, Sérgio Aurélio Ferreira. "Spatial interpolation and geostatistic simulation with the incremental Gaussian mixture network." reponame:Repositório Institucional da UFSC, 2016. https://repositorio.ufsc.br/xmlui/handle/123456789/178581.
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Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2016.Made available in DSpace on 2017-08-22T04:22:16Z (GMT). No. of bitstreams: 1 347911.pdf: 1690914 bytes, checksum: e43f9150ef3cb130f6d5696b46a68fa5 (MD5) Previous issue date: 2016
Abstract : Geostatistics aggregates a set of tools designed to deal with spatially correlated data. Two significant problems that Geostatistics tackles are the spatial interpolation and geostatistical simulation. Kriging and Sequential Gaussian Simulation (SGS) are two examples of traditional geostatistical tools used for these kinds of problems. These methods perform well when the provided Variogram is well modeled. The problem is that modeling the Variogram requires expert knowledge and a certain familiarity with the dataset. This complexity might make Geostatistics tools the last choice of a non-expert. On the other hand, an important feature present in neural networks is their ability to learn from data, even when the user does not have much information about the particular dataset. However, traditional models, such as Multilayer Perceptron (MLP), do not perform well in spatial interpolation problems due to their difficulty in accurately modeling the spatial correlation between samples. With this motivation in mind, we adapted the Incremental Gaussian Mixture Network (IGMN) model for spatial interpolation and geostatistical simulation applications. The three most important contributions of this work are: 1. An improvement in the IGMN estimation process for spatial interpolation problems with sparse datasets; 2. An algorithm to perform Sequential Gaussian Simulation using IGMN instead of Kriging; 3. An algorithm that mixes the Direct Sampling (DS) method and IGMN for cluster-based Multiple Point Simulation (MPS) with training images. Results show that our approach outperforms MLP and the original IGMN in spatial interpolation problems, especially in anisotropic and sparse datasets (in terms of RMSE and CC). Also, our algorithm for sequential simulation using IGMN instead of Kriging can generate equally probable realizations of the defined simulation grid for unconditioned simulations. Finally, our algorithm that mixes the DS method and IGMN can produce better quality simulations and runs much faster than the original DS. To the best of our knowledge, this is the first time a Neural Network model is specialized for spatial interpolation applications and can perform a geostatistical simulation.
A Geoestatística agrega um conjunto de ferramentas especializadas em dados espacialmente correlacionados. Dois problemas importantes na Geoestatística são a interpolação espacial e a simulação. A Krigagem e a Simulação Sequencial Gaussiana (SGS) são dois exemplos de ferramentas geoestatísticas utilizadas para esses tipos de problemas, respectivamente. A Krigagem e a SGS possuem bom desempenho quando o Variograma fornecido pelo usuário representa bem as correlações espaciais. O problema é que a modelagem do Variograma requer um conhecimento especializado e certa familiaridade com o conjunto de dados em estudo. Essa complexidade pode tornar difícíl a popularização dessas técnicas entre não-especialistas. Por outro lado, uma característica importante presente em Redes Neurais Artificiais é a capacidade de aprender a partir dos dados, mesmo quando o usuário não possui familiaridade com os dados. No entanto, os modelos tradicionais, como o Multilayer Perceptron (MLP), têm dificuldade em identificar a correlação espacial entre amostras e não apresentam um bom desempenho em problemas de interpolação espacial. Com essa motivação, nós adaptamos e aplicamos a Incremental Gaussian Mixture Network (IGMN) em problemas de interpolação espacial e simulação geoestatística. As três principais contribuições deste trabalho são: 1. Melhoria no processo de estimação da IGMN para problemas de interpolação espacial; 2. Um algoritmo para realizar simulação sequencial gaussiana utilizando a IGMN como interpolador; 3. Um algoritmo que mistura o método Direct Sampling (DS) e a IGMN para realizar simulação multiponto (MPS) a partir de imagens de treinamento. Os resultados mostram que a nossa abordagem é mais precisa que o MLP e a IGMN original em problemas de interpolação espacial, especialmente em conjuntos de dados esparsos e com anisotropia (em termos de RMSE e CC). Nosso algoritmo de simulação sequencial que utiliza a IGMN como interpolador é capaz de gerar simulações não condicionadas que respeitam características do conjunto original de dados. Finalmente, nosso algoritmo de simulação multiponto, que mistura o método DS e a IGMN, é capaz de realizar simulações condicionadas e produz realizações com qualidade superior num tempo de execução inferior ao do DS. Até onde sabemos, esta a primeira vez que um modelo de rede neural é especializado para aplicações de interpolação espacial e é capaz de realizar simulação geostatística.
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Zaamoune, Mehdi. "Intégration et optimisation des grilles régulières de points dans une architecture SOLAP relationnelle." Thesis, Clermont-Ferrand 2, 2015. http://www.theses.fr/2015CLF22538/document.
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Les champs continus sont des types de représentations spatiales utilisées pour modéliser des phénomènes tels que la température, la pollution ou l’altitude. Ils sont définis selon une fonction de mapping f qui affecte une valeur du phénomène étudié à chaque localisation p du domaine d’étude. Par ailleurs, la représentation des champs continus à différentes échelles ou résolutions est souvent essentielle pour une analyse spatiale efficace. L’avantage des champs continus réside dans le niveau de détails généré par la continuité, ainsi que la qualité de l’analyse spatiale fournie par la multi-résolution. L’inconvénient de ce type de représentations dans l’analyse spatio-multidimensionnelle est le coût des performances d’analyse et de stockage. Par ailleurs, les entrepôts de données spatiaux et les systèmes OLAP spatiaux (EDS et SOLAP) sont des systèmes d’aide à la décision qui permettent l’analyse spatio-multidimensionnelle de grands volumes de données spatiales et non spatiales. L’analyse des champs continus dans l’architecture SOLAP représente un défi de recherche intéressant. Différents travaux se sont intéressés à l’intégration de ce type de représentations dans le système SOLAP. Cependant, celle-ci est toujours au stade embryonnaire. Cette thèse s’intéresse à l’intégration des champs continus incomplets représentés par une grille régulière de points dans l’analyse spatio-multidimensionnelle. Cette intégration dans le système SOLAP implique que l’analyse des champs continus doit supporter : (i) les opérateurs OLAP classiques, (ii) la vue continue des données spatiales, (iii) les opérateurs spatiaux (slice spatial) et (iv) l’interrogation des données à différentes résolutions prédéfinies. Dans cette thèse nous proposons différentes approches pour l’analyse des champs continus dans le SOLAP à différents niveaux de l’architecture relationnelle, de la modélisation conceptuelle à l’optimisation des performances de calcul. Nous proposons un modèle logique FISS qui permet d’optimiser les performances d’analyse à multi-résolution en se basant sur des méthodes d’interpolation. Puis, nous exposons une méthodologie basée sur la méthode d’échantillonnage du Clustering, qui permet d’optimiser les opérations d’agrégation des grilles régulières de points dans l’architecture SOLAP relationnelle en effectuant une estimation des résultatsContinuous fields are types of spatial representations used to model phenomena such as temperature, pollution or altitude. They are defined according to a mapping function f that assigns a value of the studied phenomenon to each p location of the studied area. Moreover, the representation of continuous fields at different scales or resolutions is often essential for effective spatial analysis. The advantage of continuous fields is the level of details generated by the continuity of the spatial data, and the quality of the spatial analysis provided by the multi-resolution. The downside of this type of spatial representations in the multidimensionnal analysis is the high cost of analysis and storage performances. Moreover, spatial data warehouses and spatial OLAP systems (EDS and SOLAP) are decision support systems that enable multidimensional spatial analysis of large volumes of spatial and non-spatial data. The analysis of continuous fields in SOLAP architecture represents an interesting research challenge. Various studies have focused on the integration of such representations in SOLAP system. However, this integration still at an early stage. Thus, this thesis focuses on the integration of incomplete continuous fields represented by a regular grid of points in the spatio-multidimensional analysis. This integration in the SOLAP system involves that the analysis of continuous fields must support:(i) conventional OLAP operators, (ii) Continuous spatial data, (iii) spatial operators (spatial slice), and (iv) querying data at different predefined levels of resolutions. In this thesis we propose differents approaches for the analysis of continuous fields in SOLAP system at different levels of the relational architecture (from the conceptual modeling to the optimization of computing performance). We propose a logical model FISS to optimize the performances of the multi-resolution analysis, based on interpolation methods. Then, we present a new methodology based on the Clustering sampling method, to optimize aggregation operations on regular grids of points in the relational SOLAP architecture
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Berndt, Christian [Verfasser]. "Spatial interpolation of climate data for hydrological and environmental applications / Christian Berndt." Hannover : Technische Informationsbibliothek (TIB), 2016. http://d-nb.info/1124166823/34.
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Campbell, Kathryn Mary. "Comparing Accuracies of Spatial Interpolation Methods on 1-Minute Ground Magnetometer Readings." Thesis, North Dakota State University, 2017. https://hdl.handle.net/10365/28680.
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Geomagnetic disturbances caused by external solar events can create geomagnetically induced currents (GIC) throughout conducting networks of Earth’s surface. GIC can cause disruption that scales from minor to catastrophic. However, systems can implement preemptive measure to mitigate the effects of GICs with the use of GIC forecasting. Accurate forecasting is dependent on accurate modeling of Earth’s geomagnetic field. Unfortunately, it is not currently possible to have a measurement at every point of Earth’s field. Spatial interpolation methods can be implemented to fill in for the unmeasured space. The performances of two spatial interpolation methods, Inverse Distance Weighting and Kriging, are assessed to determine which better predicts the unmeasured space. Error testing shows both methods to be comparable, with the caveat of Kriging having a tighter precision on predictions.
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Kajornrit, Jesada. "Interpretable fuzzy systems for monthly rainfall spatial interpolation and time series prediction." Thesis, Kajornrit, Jesada (2014) Interpretable fuzzy systems for monthly rainfall spatial interpolation and time series prediction. PhD thesis, Murdoch University, 2014. https://researchrepository.murdoch.edu.au/id/eprint/26220/.
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This thesis proposes methodologies to analyze and establish interpretable fuzzy systems for monthly rainfall spatial interpolation and time series prediction. A fuzzy system has been selected due to its capability of handling the uncertainty in the data and due to its interpretability characteristic.
In the first part, this thesis proposes a methodology to analyze and establish interpretable fuzzy models for monthly rainfall spatial interpolation using global and local methods. In the global method, the proposed methodology begins with clustering analysis to de-termine the appropriate number of clusters, and fuzzy modeling and a genetic algorithm are then used to establish the fuzzy interpretation model. In the local method, the modu-lar technique has been applied to improve the accuracy of the global models while the interpretability capability of the model is maintained.
In the second part, this thesis proposes a methodology to establish single and modular interpretable fuzzy models for monthly rainfall time series predictions. In the single model, the cooperative neuro-fuzzy technique and a genetic algorithm have been used. In the modular model, the modular technique has been applied to simplify the complexi-ty of the single model. The whole system is decomposed into twelve sub-modules ac-cording to the calendar months. The proposed modular model consists of two function-ally consecutive layers, the prediction layer and the aggregation layer. In the aggregation layer, Bayesian reasoning has been applied.
The case study used in this thesis is located in the northeast region of Thailand. The proposed models were compared with commonly-used conventional and intelligent methods in the hydrological discipline. The experimental results showed that, in the quantitative aspect, the proposed models can provide good prediction accuracy and, in the qualitative aspect, the proposed models can also meet the criteria used for model in-terpretability assessment.
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Magno, Melissa A., Ingrid Luffman, Arpita Nandi, and Brian G. Evanshen. "SPATIAL INTERPOLATION OF HEAVY METAL CONCENTRATIONS IN SOILS OF BUMPUS COVE, TN." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/asrf/2018/schedule/126.
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Mining processes generate waste rock, tailings, and slag that can increase heavy metal concentrations in soils. Un-reclaimed, abandoned mine sites are particularly prone to leaching these contaminants, which may accumulate and pose significant environmental and public health concerns. The characterization and spatial delineation of heavy metals of such soils is vital for risk assessment and soil reclamation. Bumpus Cove, once one of the richest mineralized districts of eastern TN, is home to at least 47 abandoned, un-reclaimed mines that were all permanently closed by the 1950s. This study evaluated 52 soil samples collected within a 0.67 km2 study area containing 6 known abandoned Pb, Zn, and Mn mines at the headwaters of Bumpus Cove Creek for heavy metal concentrations. Soil samples were analyzed for Zn, Mn, Pb, Cu, and Cd by means of microwave-assisted acid digestion and flame atomic absorption spectrometry (FAAS). Using the measured values and digital elevation model (DEM) derived from lidar data, ordinary kriging and cokriging interpolation techniques were used to predict the trend of heavy metal concentrations throughout the study area. Concentrations for Zn, Mn, and Pb show significant variability between sample sites (ranges of 12 – 1,354 mg/kg Zn, 6 – 2,574 mg/kg Mn, 33 – 2,271 mg/kg Pb). Cu and Cd were much less variable, with ranges of 1 - 65 mg/kg and 7 – 40 mg/kg, respectively. Of the measured heavy metals, only Zn and Pb exceed permissible limits in soils. Results show that ordinary kriging interpolation methods produced improved results over ordinary cokriging with and without lognormal transformations for all metals. Mn and Pb were found to transport further downhill following the natural drainage, whereas Zn, Cu and Cd concentrations exhibit localized variability without a clear transportation path. This study can provide a reference for state and local entities responsible for heavy metal monitoring in Bumpus Cove, TN.
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Kenda, Loren Lee. "The Spatial Mismatch and Skills Mismatch Hypothesis: A Study of the Columbus Metropolitan Area Using Spatial Interpolation Methods." The Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=osu1392733133.
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Sarmah, Dipsikha. "Evaluation of Spatial Interpolation Techniques Built in the Geostatistical Analyst Using Indoor Radon Data for Ohio,USA." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1350048688.
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Weston, Emily G. "Predicting leatherback sea turtle sex ratios using spatial interpolation of nesting beach temperatures." Thesis, Florida Atlantic University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=1527434.
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Sex determination in leatherback sea turtles is directed primarily by the temperatures a clutch experiences during the middle third of development. Warmer temperatures tend to produce females will cooler temperatures yield males. Nest temperatures can vary spatially and temporally. During the 2010 and 2011 nesting seasons, this study estimated the hatchling sex ratio of leatherback sea turtles on Sandy Point National Wildlife Refuge (SPNWR), St. Croix, U.S. Virgin Islands. I measured sand temperatures from May- August and across the spatial range of leatherback nesting habitat. I spatially interpolated those temperatures to create maps that predicted temperatures for all nests incubating on SPWNR. Nest temperatures were also directly measured and compared with predicted nest temperatures to validate the prediction model. Sexes of dead-in-nest hatchlings and full term embryos were used to confirm the sex-temperature response. The model showed that microclimatic variation likely impacts the production of both sexes on SPNWR.
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Dhanasekaran, Deepananthan. "A Locally Adaptive Spatial Interpolation Technique for the Generation of High-Resolution DEMs." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1306112037.
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Chan, Tai Wai. "Applications of spatial varying filter on image interpolation, demosaicing and video denosing [i.e. denoising] /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?ECED%202006%20CHANT.
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Sterling, David L. "A Comparison of Spatial Interpolation Techniques for Determining Shoaling Rates of the Atlantic Ocean Channel." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/35072.
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The United States of Army Corp of Engineers (USACE) closely monitors the changing depths of navigation channels throughout the U.S. and Western Europe. The main issue with their surveying methodology is that the USACE surveys in linear cross sections, perpendicular to the channel direction. Depending on the channel length and width, these cross sections are spaced 100 - 400 feet apart, which produces large unmapped areas within each cross section of a survey.
Using a variety of spatial interpolation methods, depths of these unmapped areas were produced. The choice of spatial interpolator varied upon which method adequately produced surfaces from large hydrographic survey data sets with the lowest amount of prediction error. The data used for this research consisted of multibeam and singlebeam surveys. These surveys were taken in a systematic manner of linear cross-sections that produced tens of thousands of data points.
Nine interpolation techniques (inverse distance weighting, completely regularized spline, spline with tension, thin plate spline, multiquadratic spline, inverse multiquadratic spline, ordinary kriging, simple kriging, and universal kriging) were compared for their ability to accurately produce bathymetric surfaces of navigation channels. Each interpolation method was tested for effectiveness in determining depths at "unknown" areas. The level of accuracy was tested through validation and cross validation of training and test data sets for a particular hydrographic survey.
By using interpolation, grid surfaces were created at 15, 30, 60, and 90-meter resolution for each survey of the study site, the Atlantic Ocean Channel. These surfaces are used to produce shoaling amounts, which are taken in the form of volumes (yd.3). Because the Atlantic Ocean Channel is a large channel with a small gradual change in depth, a comparison of grid resolution was conducted to determine what difference, if any, exists between the calculated volumes from varying grid resolutions. Also, a comparison of TIN model volume calculations was compared to grid volume estimates.
Volumes are used to determine the amount of shoaling and at what rate shoaling is occurring in a navigation channel. Shoaling in each channel was calculated for the entire channel length. Volumes from varying grid resolutions were produced from the Atlantic Ocean Channel over a seven-year period from 1994-2001.
Using randomly arranged test and training datasets, spline with tension and thin plate spline produced the mean total error when interpolating using singlebeam and multibeam hydrographic data respectively. Thin plate spline and simple kriging produced the lowest mean total error in full cross validation testing of entire singlebeam and multibeam hydrographic datasets respectively.
Volume analysis of varying grid resolution indicates that finer grid resolution provides volume estimates comparable to TIN modeling, the USACE's technique for determining sediment volume estimates. The coarser the resolution, the less similar the volume estimates are in comparison to TIN modeling. All grid resolutions indicate that the Atlantic Ocean Channel is shoaling. Using a plan depth of 53 feet, TIN modeling displayed an annual average increase of 928,985 cubic yards of sediment from 1994 - 2001.
Master of Science
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Yu, Peng, and 于朋. "Air pollution and respiratory disease incidence of Guangzhou: a study of spatial interpolation methodsusing GIS, 2003-2004." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B41633799.
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Pfeiffer, Heiko. "Neural modelling of the spatial distribution of air pollutants a new method developed considering as example Cyprus /." [S.l. : s.n.], 2006. http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-26334.
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Yu, Peng. "Air pollution and respiratory disease incidence of Guangzhou a study of spatial interpolation methods using GIS, 2003-2004 /." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B41633799.
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Mendez, Chaves Diego. "A Framework for Participatory Sensing Systems." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4135.
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Participatory sensing (PS) systems are a new emerging sensing paradigm based on the participation of cellular users in a cooperative way. Due to the spatio-temporal granularity that a PS system can provide, it is now possible to detect and analyze events that occur at different scales, at a low cost. While PS systems present interesting characteristics, they also create new problems. Since the measuring devices are cheaper and they are in the hands of the users, PS systems face several design challenges related to the poor accuracy and high failure rate of the sensors, the possibility of malicious users tampering the data, the violation of the privacy of the users as well as methods to encourage the participation of the users, and the effective visualization of the data. This dissertation presents four main contributions in order to solve some of these challenges.
This dissertation presents a framework to guide the design and implementation of PS applications considering all these aspects. The framework consists of five modules: sample size determination, data collection, data verification, data visualization, and density maps generation modules. The remaining contributions are mapped one-on-one to three of the modules of this framework: data verification, data visualization and density maps.
Data verification, in the context of PS, consists of the process of detecting and removing spatial outliers to properly reconstruct the variables of interest. A new algorithm for spatial outliers detection and removal is proposed, implemented, and tested. This hybrid neighborhood-aware algorithm considers the uneven spatial density of the users, the number of malicious users, the level of conspiracy, and the lack of accuracy and malfunctioning sensors. The experimental results show that the proposed algorithm performs as good as the best estimator while reducing the execution time considerably.
The problem of data visualization in the context of PS application is also of special interest. The characteristics of a typical PS application imply the generation of multivariate time-space series with many gaps in time and space. Considering this, a new method is presented based on the kriging technique along with Principal Component Analysis and Independent Component Analysis. Additionally, a new technique to interpolate data in time and space is proposed, which is more appropriate for PS systems. The results indicate that the accuracy of the estimates improves with the amount of data, i.e., one variable, multiple variables, and space and time data. Also, the results clearly show the advantage of a PS system compared with a traditional measuring system in terms of the precision and spatial resolution of the information provided to the users.
One key challenge in PS systems is that of the determination of the locations and number of users where to obtain samples from so that the variables of interest can be accurately represented with a low number of participants. To address this challenge, the use of density maps is proposed, a technique that is based on the current estimations of the variable. The density maps are then utilized by the incentive mechanism in order to encourage the participation of those users indicated in the map. The experimental results show how the density maps greatly improve the quality of the estimations while maintaining a stable and low total number of users in the system.
P-Sense, a PS system to monitor pollution levels, has been implemented and tested, and is used as a validation example for all the contributions presented here. P-Sense integrates gas and environmental sensors with a cell phone, in order to monitor air quality levels.
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Bhatta, Aman. "INTEGRATING REMOTE SENSING TO IMPROVE CROP GRAIN YIELD ESTIMATES FOR ASSESSING WITHIN-FIELD SPATIAL AND TEMPORAL VARIABILITY." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1594060903495952.
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Akyildiz, Isin. "Drection Of Arrival Estimation By Array Interpolation In Randomly Distributed Sensor Arrays." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607963/index.pdf.
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In this thesis, DOA estimation using array interpolation in randomly distributed sensor arrays is considered. Array interpolation is a technique in which a virtual array is obtained from the real array and the outputs of the virtual array, computed from the real array using a linear transformation, is used for direction of arrival estimation. The idea of array interpolation techniques is to make simplified and computationally less demanding high resolution
direction finding methods applicable to the general class of
non-structured arrays.In this study,we apply an interpolation
technique for arbitrary array geometries in an attempt to extend root-MUSIC algorithm to arbitrary array geometries.Another issue of array interpolation related to direction finding is spatial smoothing in the presence of multipath sources.It is shown that due to the Vandermonde structure of virtual array manifold vector obtained from the proposed interpolation method, it is possible to use spatial smoothing algorithms for the case of multipath sources.
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Ji, Wei. "Spatial Partitioning and Functional Shape Matched Deformation Algorithm for Interactive Haptic Modeling." Ohio University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1226364059.
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Jega, Idris Mohammed. "Estimating population surfaces in areas where actual distributions are unknown : dasymetric mapping and pycnophylactic interpolation across different spatial scales." Thesis, University of Leicester, 2015. http://hdl.handle.net/2381/32529.
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Spatially distributed estimates of population provide commonly used demand surfaces in support of spatial planning. In many countries, spatially detailed population summaries are not available. For such cases a number of interpolation methods have been proposed to redistribute summary population totals over small areas. Population allocations to small areas are commonly validated by comparing the estimates with some known values for those areas. In areas where spatially detailed estimates of the population do not exist, that is where the actual population in small areas is unknown, such as Nigeria validation is problematic. This research explores different interpolation methods applied at different scales in areas where the actual population distribution is known and where validation is possible. It then applies the parameters developed from these results to areas where the distribution is unknown. The binary dasymetric method using land cover data derived from a classified 30m spatial resolution satellite imagery as the ancillary data input and with disaggregation over 30m support grids, was found to provide the best target zones estimates of the population. The demand surfaces were then used to evaluate current health facility locations and then to suggest alternative spatial arrangements for health centres in Port-Harcourt, Nigeria. The average distance from each demand point to the nearest healthcare centre was found to be 1204m. When alternative locations for the current health centres were identified, the results suggest 13 service provision points would provide almost the same demand coverage as the 17 current PHCCs. This research develops methods that can be used to support informed decision making in spatial planning and policy development.
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Oesting, Marco [Verfasser], Martin [Akademischer Betreuer] Schlather, and Robert [Akademischer Betreuer] Schaback. "Spatial Interpolation and Prediction of Gaussian and Max-Stable Processes / Marco Oesting. Gutachter: Martin Schlather ; Robert Schaback. Betreuer: Martin Schlather." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2012. http://d-nb.info/1042970890/34.
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Thomas, Zachary Micah. "Bayesian Hierarchical Space-Time Clustering Methods." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1435324379.
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Goergens, Chad A. "20th Century Antarctic Pressure Variability and Trends Using a Seasonal Spatial Pressure Reconstruction." Ohio University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1491474158428459.
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Silase, Geletu Biruk. "Modeling the Behavior of an Electronically Switchable Directional Antenna for Wireless Sensor Networks." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3026.
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Reducing power consumption is among the top concerns in Wireless Sensor Networks, as the lifetime of a Wireless Sensor Network depends on its power consumption. Directional antennas help achieve this goal contrary to the commonly used omnidirectional antennas that radiate electromagnetic power equally in all directions, by concentrating the radiated electromagnetic power only in particular directions. This enables increased communication range at no additional energy cost and reduces contention on the wireless medium. The SPIDA (SICS Parasitic Interference Directional Antenna) prototype is one of the few real-world prototypes of electronically switchable directional antennas for Wireless Sensor Networks. However, building several prototypes of SPIDA and conducting real-world experiments using them may be expensive and impractical. Modeling SPIDA based on real-world experiments avoids the expenses incurred by enabling simulation of large networks equipped with SPIDA. Such a model would then allow researchers to develop new algorithms and protocols that take advantage of the provided directional communication on existing Wireless Sensor Network simulators. In this thesis, a model of SPIDA for Wireless Sensor Networks is built based on thoroughly designed real-world experiments. The thesis builds a probabilistic model that accounts for variations in measurements, imperfections in the prototype construction, and fluctuations in experimental settings that affect the values of the measured metrics. The model can be integrated into existing Wireless Sensor Network simulators to foster the research of new algorithms and protocols that take advantage of directional communication. The model returns the values of signal strength and packet reception rate from a node equipped with SPIDA at a certain point in space given the two-dimensional distance coordinates of the point and the configuration of SPIDA as inputs.Phone:+46765816263 Additional email: burkaja@yahoo.com
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Yu, Hao. "Spatial and temporal population dynamics of yellow perch (Perca flavescens) in Lake Erie." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/28586.
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Yellow perch (Perca flavescens) in Lake Erie support valuable commercial and recreational fisheries critical to the local economy and society. The study of yellow perch's temporal and spatial population dynamics is important for both stock assessment and fisheries management. I explore the spatial and temporal variation of the yellow perch population by analyzing the fishery-independent surveys in Lake Erie. Model-based approaches were developed to estimate the relative abundance index, which reflected the temporal variation of the population. I also used design-based approaches to deal with the situation in which population density varied both spatially and temporally.
I first used model-based approaches to explore the spatial and temporal variation of the yellow perch population and to develop the relative abundance index needed. Generalized linear models (GLM), spatial generalized linear models (s-GLM), and generalized additive models (GAM) were compared by examining the goodness-of-fit, reduction of spatial autocorrelation, and prediction errors from cross-validation. The relationship between yellow perch density distribution and spatial and environmental factors was also studied. I found that GAM showed the best goodness-of-fit shown as AIC and lowest prediction errors but s-GLM resulted in the best reduction of spatial autocorrelation. Both performed better than GLM for yellow perch relative abundance index estimation. I then applied design-based approaches to study the spatial and temporal population dynamics of yellow perch through both practical data analysis and simulation. The currently used approach in Lake Erie is stratified random sampling (StRS). Traditional sampling designs (simple random sampling (SRS) and StRS) and adaptive sampling designs (adaptive two-phase sampling (ATS), adaptive cluster sampling (ACS), and adaptive two-stage sequential sampling (ATSS)) for fishery-independent surveys were compared. From accuracy and precision aspect, ATS performed better than the SRS, StRS, ACS and ATSS for yellow perch fishery-independent survey data in Lake Erie. Model-based approaches were further studied by including geostatistical models. The performance of the GLM and GAM models and geostatistical models (spatial interpolation) were compared when they are used to analyze the temporal and spatial variation of the yellow perch population through a simulation study. This is the first time that these two types of model- based approaches have been compared in fisheries. I found that arithmetic mean (AM) method was only preferred when neither environment factors nor spatial information of sampling locations were available. If the survey can not cover the distribution area of the population due to biased design or lack of sampling locations, GLMs and GAMs are preferable to spatial interpolation (SI). Otherwise, SI is a good alternative model to estimate relative abundance index. SI has rarely been realized in fisheries.
Different models may be recommended for different species/fisheries when we estimate their spatial-temporal dynamics, and also the most appropriate survey designs may be different for different species. However, the criteria and approaches for the comparison of both model-based and design-based approaches will be applied for different species or fisheries.Ph. D.
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Martins, Ivan Carlos Fernandes [UNESP]. "Insecta e Arachnida associados ao solo: plantas herbáceas como área de refúgio visando ao controle biológico conservativo." Universidade Estadual Paulista (UNESP), 2011. http://hdl.handle.net/11449/102309.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Este trabalho teve como objetivo principal avaliar o controle biológico conservativo através da criação de área de refúgio em um agroecossistema. A área de estudo foi estabelecida em um hectare com a área de refúgio apresentando 80 m de comprimento e constituída por quatro canteiros de 20 m, cada um deles contendo uma das seguintes espécies de planta herbácea perene: Panicum maximum cv. Massai e Cynodon spp. cv. Tifton 85 (gramíneas) e Stylosanthes spp. cv. BRS Campo Grande e Calopogonium mucunoides cv. Comum (leguminosas). Os artrópodes foram amostrados por meio de armadilhas de solo tipo alçapão. Todas as análises foram realizadas com as espécies consideradas predominantes classificadas de acordo com a abundância, freqüência, constância e dominância. Utilizou-se análise de regressão múltipla com seleção de variáveis “stepwise” para verificar a influência dos fatores meteorológicos na variação populacional. As fases fenológicas da soja e milho foram determinadas e relacionadas com a flutuação populacional. Para determinar a distribuição espacial os dados foram analisados através dos índices de dispersão e modelos probabilísticos. A visualização da distribuição e influência da área de refúgio foi verificada por mapa de interpolação linear. Um total de 79.633 espécimes e 514 espécies de artrópodes foram coletados. Os himenópteros e os coleópteros foram os grupos mais diversificados e abundantes, com destaque para os formicídeos e carabídeos. Os refúgios com as plantas Stylosanthes spp. e Panicum maximum apresentaram maior diversidade e abundância de artrópodes. A maioria dos artrópodes associados ao solo considerados predominantes apresentou distribuição agregada. Muitos destes, principalmente artrópodes predadores, se agruparam próximo ou na área de refúgio
The objective of this study was to evaluate the conservation biological control through the creation of beetle bank in an agroecosystem. The study was conducted in one hectare with a 80 m long refuge area, with four blocks of 20 m., in each block one species of perennial herbaceous plant was planted: Panicum maximum cv. Masai and Cynodon spp. cv. Tifton 85 (grasses) and Stylosanthes spp. cv. BRS Campo Grande and Calopogonium mucunoides cv. Common (legumes). The Arthropods were sampled by pitfall traps. All analyses were performed with the predominant species considered classified according to the abundance, frequency, constancy, and dominance. We used multiple regression analysis with variable selection stepwise to assess the influence of meteorological factors in population. The soybean and corn phenological stages were determined and related to population fluctuation. To determine the spatial distribution, data were analyzed using dispersion indices and probabilistic models based on the frequency distribution of the arthropods. The illustration of the distribution and influence of the beetle bank was verified by linear interpolation map. A total of 79,633 specimens and 514 species of arthropods were collected. The Hymenoptera and Coleoptera were more diverse and abundant, specially ants and ground beetles. The refuges with plants Stylosanthes spp. and Panicum maximum showed greater diversity and abundance of arthropods. Aggregated distribution was showed for most predominant arthropods associated with soil. Many of these, mainly predatory arthropods, clustered near or in the beetle bank
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Wolff, Wagner. "Avaliação e nova proposta de regionalização hidrológica para o Estado de São Paulo." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/11/11152/tde-08042013-102503/.
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A regionalização hidrológica é uma técnica que permite transferir informação entre bacias hidrográficas semelhantes, a fim de calcular em sítios que não dispõem de dados, as variáveis hidrológicas de interesse; assim, a mesma caracteriza-se por ser uma ferramenta útil na obtenção de outorga de direitos de uso de recursos hídricos, instrumento previsto na Lei 9433/97. Devido à desatualização do modelo atual de regionalização hidrológica do Estado de São Paulo, proposto na década de 80, este estudo tem como objetivo geral avaliar se o mesmo está adequado ao uso, de acordo com a atualização de seu banco de dados, e propor um novo que supere as limitações do antigo. O estudo foi realizado no Estado de São Paulo, que tem área de aproximadamente 248197 km², localizado entre as longitudes -44° 9\', e -53º 5\', e entre as latitudes -22° 40\', e -22° 39\'. Utilizou-se, inicialmente, dados de 176 estações fluviométricas administradas pelo DAEE e pela ANA, disponíveis em http://www.sigrh.sp.gov.br. Determinou-se para as estações, a precipitação média anual da bacia hidrográfica (P), a vazão média plurianual (Q), a vazão mínima média de 7 dias seguidos com período de retorno de 10 anos (Q7,10) e as vazões com 90 e 95% de permanência no tempo (Q90 e Q95). Posteriormente, fez-se análise de consistência excluindo as estações inconsistentes do estudo; assim, restaram 172 para serem utilizadas na avaliação do modelo e formulação de um novo. A avaliação do modelo fez-se pelo índice de confiança (c), que é definido pelo produto entre o coeficiente de correlação (r) e o índice de concordância (d), utilizando como valor de estimativa as vazões geradas pelo modelo, e como valor padrão as calculadas por intermédio das estações fluviométricas. Todas as vazões avaliadas foram classificadas como ótimas, com índice de confiança (c) acima de 0,85; assim, o atual modelo rejeitou a hipótese de que a atualização de seu banco de dados pudesse inferir em sua capacidade preditiva; portanto, o mesmo pode ser usado na obtenção das vazões estudadas que são referência na emissão de outorga em diferentes Estados do Brasil. Entretanto, o modelo apresentou algumas limitações, como extrapolação para áreas de bacias de drenagem menores do que as utilizadas para formulá-lo, e problemas em seu aplicativo computacional: o mesmo informa a precipitação média anual na coordenada geográfica do local de captação da água, e não da bacia de drenagem a montante do referido local. Neste enfoque, foi formulado um novo modelo, que superou as limitações e proporcionou capacidade preditiva maior que a do antigo.A hydrological regionalization is a technique that allows to transfer information between similar watersheds in order to calculate, in sites where there are no data on the hydrological variables of interest. This technique becomes a useful tool to ensure the rights of water resources use, instrument provided by Law 9433/97. Due to the outdated hydrological regionalization model of São Paulo State, proposed in the 1980\'s, this study aims to broadly assess whether the current model is appropriate to use, according to an analysis of its update database and to propose a new model to overcome the limitations of the current one. The study was conducted in State São Paulo with area of approximately 248197 km ², located between longitudes -44 ° 9 \', and -53 ° 5\', and between latitudes 40 ° -22\' and -22 ° 39\'. We used data from 176 initially gauged stations administered by ANA and DAEE available at http://www.sigrh.sp.gov.br, where it was determined to the stations, the average annual rainfall of the basin (P) multiannual average streamflow (Q), streamflow minimum average of 7 consecutive days with a return period of 10 years (Q7,10) and streamflows with 90 and 95% of permanence in time(Q90 e Q95). Afterwards, we analyzed the consistency excluding the inconsistent stations from the study, thus, remaining 172 to be used in the model evaluation and development of a new model. The model evaluation was made by the confidence index (c), which is the product between the correlation coefficient (r) and the agreement index (d), using as estimate value the streamflows generated by the model and as the standard value, the streamflows calculated through the gauged stations. All streamflows evaluated were classified as optimal, with confidence index (c) above 0.85, therefore, the current model rejected the hypothesis that upgrading the database could infer its predictive ability, so, it can be used to obtain the streamflows studied that refer to use grants in different States of Brazil. However, the model had some limitations, such as extrapolation to areas of smaller watersheds than those used to formulate it, and computer application problems, being that, it reports the average annual precipitation at the geographic coordinate at the local catchment water, not the watershed upstream of that location. A new model was formulated that surpasses the limitations and provides greater predictive ability than the current one.
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Montgomery, Marilyn Christina. "Assessing the Environmental Justice Implications of Flood Hazards in Miami, Florida." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5276.
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While environmental justice (EJ) research in the U.S. has traditionally focused on inequities in the distribution of technological hazards, the disproportionate impacts of Hurricane Katrina on racial minorities and socioeconomically disadvantaged households have prompted researchers to investigate the EJ implications of natural hazards such as flooding. Recent EJ research has also emphasized the need to examine social inequities in access to environmental amenities. Unlike technological hazards such as air pollution and toxic waste sites, areas exposed to natural hazards such as hurricanes and floods have indivisible amenities associated with them. Coastal property owners are exposed to flood hazards, but also enjoy water views and unhampered access to oceans and the unique recreational opportunities that beaches offer. Conversely, dense urban development and associated impervious surfaces increase likelihood of floods in inland areas which may lack the amenities of proximity to open water.
This dissertation contributes to the emerging literature on EJ and social vulnerability to natural hazards by analyzing racial, ethnic, and socioeconomic inequities in the distribution of flood risk exposure in the Miami Metropolitan Statistical Area (MSA), Florida--one of the most hurricane-prone areas in the world and one of the most ethnically diverse MSAs in the U.S. The case study evaluates the EJ implications of residential exposure to coastal flood risk, inland flood risk, and no flood risk, in conjunction with coastal water related amenities, using geographic information science (GIS)-based techniques and logistic regression modeling to estimate flood risk exposure. Geospatial data from the Federal Emergency Management Agency (FEMA) are utilized to delineate coastal and inland 100-year flood hazard zones. Socio-demographic variables previously utilized in EJ research are obtained from tract level data published in the 2010 census and 2007-2011 American Community Survey five-year estimates. Principal components analysis is employed to condense several socio-demographic attributes into two neighborhood deprivation indices that represent economic insecurity and instability, respectively. Indivisible coastal water related amenities are represented by control variables of percent seasonal homes and proximity to public beach access sites. Results indicate that racial/ethnic minorities and those with greater social vulnerability based on the neighborhood deprivation indices are more likely to reside in inland flood zones and areas outside 100-year flood zones, while residents in coastal flood zones are disproportionately non-Hispanic White. Moreover, residents exposed to coastal flood risk tend to live in areas with ample coastal water related amenities, while racial/ethnic minorities and individuals with higher neighborhood deprivation who are exposed to inland flood risk or no flood risk reside in areas without coastal water related amenities. This dissertation elucidates the importance of EJ research on privilege and access to environmental amenities in conjunction with environmental hazards because areas exposed to natural hazards are likely to offer indivisible benefits.
Estimating people and places exposed to hazards for EJ research becomes difficult when the boundaries of census areal units containing socio-demographic data do not match the boundaries of hazard exposure areas. This challenge is addressed with an application of dasymetric spatial interpolation using GIS-based techniques to disaggregate census tracts to inhabited parcels. Several spatial interpolation methods are assessed for relative accuracy in estimating population densities for the Miami MSA, and the output units from the most accurate method are employed in EJ regression analyses. The dasymetric mapping efforts utilized herein contribute to research on the modifiable areal unit problem (MAUP) and its effects on statistical analyses. Since the dasymetric mapping technique used for EJ analyses disaggregates census tracts to the inhabited parcel level, the results of the associated analyses for flood hazards exposure and access to coastal water related amenities should be more reliable than those based on tracts. The enhanced accuracy associated with inhabited parcels is a result of using a more precise geospatial depiction of residential populations, which leads to a more accurate portrayal of disproportionate exposure to flood hazards. Consequently, this dissertation contributes methodologically to GIS-based techniques of dasymetric spatial interpolation and empirically to EJ analysis of flood hazards with indivisible coastal water related amenities.
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Pinto, Leandro de Mello. "ALTIMETRIA COM TOPOGRAFIA CONVENCIONAL E SENSORIAMENTO REMOTO." Universidade Federal de Santa Maria, 2012. http://repositorio.ufsm.br/handle/1/9559.
Full textAbstract:
Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorThe altimetry of the surface terrain for many decades has been achieved almost exclusively by conventional surveying. The advancement of technology allowed the development of space missions and the creation of artificial satellites, making the science of remote sensing to expand exponentially. The SRTM (Shuttle Radar Topographic Mission) and ASTER GDEM (Advanced Spaceborne Thermal Emission and Reflection Radiometer) are spatial programs that provide altitude information of almost the entire globe. The program Google Earth uses that information, and provides for its users in a practical and rapid way. For ease of access to data from these techniques, many users use them without knowing the geometric problems existing in these products, which can compromise the quality of results obtained through these techniques. Therefore, there is a need for a prior evaluation to assess the quality and workability for each method. In this context, the objective was to analyze the accuracy of three ways of obtaining altitude: by SRTM, ASTER and Google Earth, comparing them with conventional surveying and with GPS, because they are more established techniques. To this end, two surveys were performed in situ, one using a GPS receiver and the other by conventional topography, where the heights of the points were compared with the heights obtained by the three methods analyzed, resulting in discrepancies. The results show that the data from the SRTM provided by Embrapa Monitoring by Satellites through the Project Brasil em Relevo are more accurate than ASTER data and Google Earth, moreover, was also found that the Kriging interpolation technique has best results for altimetric spatial data.
A altimetria do terreno, por muitas décadas, tem sido obtida, quase que exclusivamente por meio da topografia convencional. O avanço da tecnologia permitiu o desenvolvimento de missões espaciais e a criação de satélites artificiais, fazendo com que a ciência do Sensoriamento Remoto se expandisse de forma exponencial. O SRTM (Shuttle Radar Topographic Mission) e o ASTER GDEM (Advanced Spaceborne Thermal Emission and Reflection Radiometer) são programas espaciais que fornecem informações altimétricas de quase todo o globo terrestre. O programa Google Earth utiliza-se dessas informações e as disponibiliza para seus usuários de forma prática e rápida. Pela facilidade de acesso aos dados provenientes destas técnicas, muitos usuários os utilizam sem conhecer os problemas geométricos existentes nesses produtos, o que pode comprometer a qualidade dos resultados obtidos através dessas técnicas. Por esse motivo, há a necessidade de uma prévia avaliação para aferir a qualidade e a aplicabilidade de cada método. Nesse contexto, o objetivo do trabalho foi analisar a acurácia de três formas de obtenção de altitude: por SRTM, ASTER e Google Earth, comparando-os com a topografia convencional e com o GPS, por serem técnicas mais consolidadas. Para isso, foram realizados dois levantamentos in situ, um através de receptores GPS e outro por topografia convencional, onde as altitudes dos pontos foram comparadas com as altitudes obtidas pelos três métodos analisados, resultando nas discrepâncias. Os resultados mostram que os dados provenientes do SRTM, fornecidos pela Embrapa Monitoramento por Satélite, através do Projeto Brasil em Relevo, são mais acurados do que os dados ASTER e Google Earth, além disto, também foi constatado que a técnica de interpolação da Krigagem apresenta melhores resultados para a espacialização de dados altimétricos.
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Wang, Xiao-Yu. "Spatial analysis of long-term exposure to air pollution and cardiorespiratory mortality in Brisbane, Australia." Queensland University of Technology, 2008. http://eprints.qut.edu.au/16627/.
Full textAbstract:
Air pollution is ranked by the World Health Organisation as one of the top ten contributors to the global burden of disease and injury. Epidemiological studies have shown that exposure to air pollution is associated with cardiorespiratory diseases. However, most of the previous studies have looked at this issue using air pollution data from a single monitoring site or average values from a few monitoring sites in a city. There is increasing concern that the relationships between air pollution and mortality may vary with geographical area, particularly for a big city. This thesis consisted of three interlinked studies that aimed to examine the spatial variation in the relationship between long-term exposure to air pollution and cardiorespiratory mortality in Brisbane, Australia. The first study evaluated the long-term air pollution trends in Brisbane, Australia. Air pollution data used in this study were provided by the Queensland Environmental Protection Agency (QEPA). The data comprised the daily average concentrations of particulate matter less then 10 µm in aerodynamic diameter (PM10), nitrogen dioxide (NO2), ozone (O3) and sulphur dioxide (SO2) between 1 January 1980 and 31 December 2004 in two monitoring sites (i.e. Eagle farm and Rocklea), and in other available monitoring sites between 1 January 1996 and 31 December 2004. Computerised data files of daily mortality between 1 January 1996 and 31 December 2004 in Brisbane city were provided by the Office of Economic and Statistical Research of the Queensland Treasury. Population data and the Socio-Economic Indexes for Areas (SEIFA) data in 2001 were obtained from the Australian Bureau of Statistics (ABS) for each statistical local area (SLA) of the Brisbane city. The long-term air pollution (the daily maximum 1-hour average or daily 24-hour average concentrations of NO2, O3 and PM10) trends were evaluated using a polynomial regression model in two monitoring sites (Eagle Farm and Rocklea) in Brisbane, Australia, between 1980 and 2003. The study found that there were significant up-and-down features for air pollution concentrations in both monitoring sites in Brisbane. Rocklea recorded a substantially higher number of days with concentrations above the relevant daily maximum 1-hour or 24-hour standards than that in Eagle Farm. Additionally, there was a significant spatial variation in air pollution concentrations between these areas. Therefore, the results indicated a need to examine the spatial variation in the relationship between long-term exposure to air pollution and cardiorespiratory mortality in Brisbane. The second study examined the spatial variation of SO2 concentrations and cardiorespiratory mortality in Brisbane between 1999 and 2001. Air pollutant concentrations were estimated using geographical information systems (GIS) techniques at a SLA level. Spatial distribution analysis and a multivariable logistic regression model were employed to investigate the impact of gaseous air pollution on cardiorespiratory mortality after adjusting for potential confounding effects of age, sex, calendar year and SEIFA. The results of this study indicate that for every 1 ppb increase in annual average SO2 concentration, there was an estimated increase of 4.4 % (95 % confidence interval (CI): 1.4 - 7.6 %) and 4.8 % (95 % CI: 2.0 - 7.7 %) in cardiovascular and cardiorespiratory mortality, respectively. We estimated that the excess number of cardiorespiratory deaths attributable to SO2 was 312 (3.4% of total cardiorespiratory deaths) in Brisbane during the study period. Our results suggest that long-term exposure to SO2, even at low levels, is a significant hazard to population health. The final study examined the association of long-term exposure to gaseous air pollution (including NO2, O3 and SO2) with cardiorespiratory mortality in Brisbane, Australia, 1996 - 2004. The pollutant concentrations were estimated using GIS techniques at a SLA level. Logistic regression was used to investigate the impact of NO2, O3 and SO2 on cardiorespiratory mortality after adjusting for potential confounding effects of age, sex, calendar year and SEIFA. The study found that there was an estimated 3.1% (95% CI: 0.4 - 5.8%) and 0.5% (95% CI: -0.03 - 1.3 %) increase in cardiorespiratory mortality for 1 ppb increment in annual average concentration of SO2 and O3, respectively. However there was no significant relationship between NO2 and cardiorespiratory mortality observed in the multiple gaseous pollutants model. The results also indicated that long-term exposure to gaseous air pollutants in Brisbane, even at the levels lower than most cities in the world (especially SO2), were associated with cardiorespiratory mortality. Therefore, spatial patterns of gaseous air pollutants and their impact on health outcomes need to be assessed for an evaluation of long-term effects of air pollution on population health in metropolitan areas. This study examined the relationship between air pollution and health outcomes. GIS and relevant mapping technologies were used to display the spatial patterns of air pollution and cardiorespiratory mortality at a SLA level. The results of this study show that long-term exposure to gaseous air pollution was associated with cardiorespiratory mortality in Brisbane and this association appeared to vary with geographic area. These findings may have important public health implications in the control and prevention of air pollution-related health effects, since now many countries and governments have paid more attention to control wide spread air pollution and to protect our environment and human health.
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Wang, Xiao Yu. "Spatial analysis of long-term exposure to air pollution and cardiorespiratory mortality in Brisbane, Australia." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/16627/1/Xiao-Yu_Wang_Thesis.pdf.
Full textAbstract:
Air pollution is ranked by the World Health Organisation as one of the top ten contributors to the global burden of disease and injury. Epidemiological studies have shown that exposure to air pollution is associated with cardiorespiratory diseases. However, most of the previous studies have looked at this issue using air pollution data from a single monitoring site or average values from a few monitoring sites in a city. There is increasing concern that the relationships between air pollution and mortality may vary with geographical area, particularly for a big city. This thesis consisted of three interlinked studies that aimed to examine the spatial variation in the relationship between long-term exposure to air pollution and cardiorespiratory mortality in Brisbane, Australia.
The first study evaluated the long-term air pollution trends in Brisbane, Australia. Air pollution data used in this study were provided by the Queensland Environmental Protection Agency (QEPA). The data comprised the daily average concentrations of particulate matter less then 10 µm in aerodynamic diameter (PM10), nitrogen dioxide (NO2), ozone (O3) and sulphur dioxide (SO2) between 1 January 1980 and 31 December 2004 in two monitoring sites (i.e. Eagle farm and Rocklea), and in other available monitoring sites between 1 January 1996 and 31 December 2004. Computerised data files of daily mortality between 1 January 1996 and 31 December 2004 in Brisbane city were provided by the Office of Economic and Statistical Research of the Queensland Treasury. Population data and the Socio-Economic Indexes for Areas (SEIFA) data in 2001 were obtained from the Australian Bureau of Statistics (ABS) for each statistical local area (SLA) of the Brisbane city.
The long-term air pollution (the daily maximum 1-hour average or daily 24-hour average concentrations of NO2, O3 and PM10) trends were evaluated using a polynomial regression model in two monitoring sites (Eagle Farm and Rocklea) in Brisbane, Australia, between 1980 and 2003. The study found that there were significant up-and-down features for air pollution concentrations in both monitoring sites in Brisbane. Rocklea recorded a substantially higher number of days with concentrations above the relevant daily maximum 1-hour or 24-hour standards than that in Eagle Farm. Additionally, there was a significant spatial variation in air pollution concentrations between these areas. Therefore, the results indicated a need to examine the spatial variation in the relationship between long-term exposure to air pollution and cardiorespiratory mortality in Brisbane.
The second study examined the spatial variation of SO2 concentrations and cardiorespiratory mortality in Brisbane between 1999 and 2001. Air pollutant concentrations were estimated using geographical information systems (GIS) techniques at a SLA level. Spatial distribution analysis and a multivariable logistic regression model were employed to investigate the impact of gaseous air pollution on cardiorespiratory mortality after adjusting for potential confounding effects of age, sex, calendar year and SEIFA. The results of this study indicate that for every 1 ppb increase in annual average SO2 concentration, there was an estimated increase of 4.4 % (95 % confidence interval (CI): 1.4 - 7.6 %) and 4.8 % (95 % CI: 2.0 - 7.7 %) in cardiovascular and cardiorespiratory mortality, respectively. We estimated that the excess number of cardiorespiratory deaths attributable to SO2 was 312 (3.4% of total cardiorespiratory deaths) in Brisbane during the study period. Our results suggest that long-term exposure to SO2, even at low levels, is a significant hazard to population health.
The final study examined the association of long-term exposure to gaseous air pollution (including NO2, O3 and SO2) with cardiorespiratory mortality in Brisbane, Australia, 1996 - 2004. The pollutant concentrations were estimated using GIS techniques at a SLA level. Logistic regression was used to investigate the impact of NO2, O3 and SO2 on cardiorespiratory mortality after adjusting for potential confounding effects of age, sex, calendar year and SEIFA. The study found that there was an estimated 3.1% (95% CI: 0.4 - 5.8%) and 0.5% (95% CI: -0.03 - 1.3 %) increase in cardiorespiratory mortality for 1 ppb increment in annual average concentration of SO2 and O3, respectively. However there was no significant relationship between NO2 and cardiorespiratory mortality observed in the multiple gaseous pollutants model. The results also indicated that long-term exposure to gaseous air pollutants in Brisbane, even at the levels lower than most cities in the world (especially SO2), were associated with cardiorespiratory mortality.
Therefore, spatial patterns of gaseous air pollutants and their impact on health outcomes need to be assessed for an evaluation of long-term effects of air pollution on population health in metropolitan areas.
This study examined the relationship between air pollution and health outcomes. GIS and relevant mapping technologies were used to display the spatial patterns of air pollution and cardiorespiratory mortality at a SLA level. The results of this study show that long-term exposure to gaseous air pollution was associated with cardiorespiratory mortality in Brisbane and this association appeared to vary with geographic area. These findings may have important public health implications in the control and prevention of air pollution-related health effects, since now many countries and governments have paid more attention to control wide spread air pollution and to protect our environment and human health.
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Anderson, Victoria, Isaac Shockley, Arpita Nandi, and Ingrid Luffman. "Geostatistical Approach to Delineate Wetland Boundaries in the Cutshaw Bog, Tennessee." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/asrf/2018/schedule/37.
Full textAbstract:
Wetlands are one of the most productive ecosystems in the world, providing a range of services, including: water quality improvement, flood mitigation, erosion control, habitat, and carbon storage. It is estimated that Tennessee has lost 60% of its original 2 million acres of pre-European settlement wetlands. Recently, increased funding has been made available for wetland restoration and expansion. In response, the Cherokee National Forest has proposed a range of wetland restoration actions within the Paint Creek Watershed to expand and restore some of the existing bogs and fens, including the Cutshaw Bog, a 163,864 m2 wetland located 32 km south of Greeneville, TN. The U.S. Forest Service has proposed a new expanded wetland boundary to result from restoration efforts. However, to assess the potential for success, current wetland indicators based on soil color, texture, depth, drainage, sulfide materials, and iron concentrations were examined. Sampling locations were identified by overlaying a grid, composed of 64 cells, each 40.5 meter by 40.5 meter in size. Soil cores were extracted up to a depth of 0.6 meters from each sampling cell and evaluated in situ for hydric soil properties using the Eastern Mountains and Piedmont Army Corps of Engineers Wetlands Delineation Manual. Soil physical (texture, bulk density, moisture content) and chemical (pH, cation exchange capacity, % base saturation, Nitrogen, Bray II Phosphorus, Iron, Zinc, and Total Carbon Content) properties were evaluated in the laboratory. Results indicated 47% of samples taken within the proposed wetland expansion area currently have hydric soil characteristics and were located along drainage lines. Presence of hydric soils was correlated with soil physicochemical properties including bulk density, moisture content, sulfur and phosphorus concentrations, iron, and other metals. Statistical analyses for the northern section and southern section of the bog were completed separately, as they were physically divided by a French drain structure. Logistic regression models were developed using properties most strongly correlated with the presence of hydric soil. For the northern section, bulk density and iron were retained in the model, while for the southern section, iron was retained. A spatial model for the presence of hydric soil was developed by spatially interpolating the covariates through kriging. Next, a probability map was created from the logistic regression equation with raster math in ArcGIS Pro. Results indicate that Cutshaw Bog’s area cannot be expanded to the original proposed boundary provided by the US Forest Service and a new recommended boundary was delineated from the probability map. The results of this data driven approach will assist the Forest Service in targeted wetland restoration efforts at the Cutshaw Bog.
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Ferraz, Rafael Camargo. "DESENVOLVIMENTO DE UM SISTEMA WEB PARA ESTIMATIVA NUMÉRICA DE DADOS METEOROLÓGICOS DO RIO GRANDE DO SUL." Universidade Federal de Santa Maria, 2010. http://repositorio.ufsm.br/handle/1/9527.
Full textAbstract:
Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorClimate influences a large variety of human activities and the real-time access to climatic data aims for providing information that are fundamental in several human activities, mainly for agriculture. Nowadays there are few weather stations operating, which causes a lack of information at worldwide level, in different regions. Keeping in mind that the state of Rio Grande do Sul has a large part of its economy based on agriculture, as well as the climatological information's relevance, this study aims at developing a web system for numerical estimate of meteorological data in the RS's state, based on the automatical and superficial weather stations from the National Institute of Meteorology (INMET), in order to make the data available to the regions where there are no stations. The inverse distance weighting was used as an interpolation model, applying the exponents from zero (0) to five (5) and, later, comparing them through the coefficients of correlation, regression and the performance index. It was made use of programming languages as PHP, HTML and JavaScript to develop the web system, with support for MySQL database. The programs used were Macromedia Dreamweaver 8.0 and HeidSQL; the first was used for web programming whereas the second was used to manage the database. Among the nine variables that was analysed, just four of them showed a great performance. They are: temperature, relative humidity, atmospheric pressure and dew points. The interpolation model with the exponent five (5) has shown the best performance regarding to the four variables. After defining the best method, it was created a MySQL database called SWIM (Meteorological Interpolation's Web System) and through it the web system was developed, which has offered quickness, security and reliability to the application.
O clima influencia as mais diversas atividades humanas e o acesso aos dados climatológicos em tempo real, visa o fornecimento de informações que são fundamentais, principalmente para a agricultura. Atualmente existem poucas estações meteorológicas instaladas o que gera carência de informações, em âmbito mundial, para diversas regiões. Tendo em vista que o Estado do Rio Grande do Sul possui grande parte de sua economia baseada na agricultura e também a relevância das informações climatológicas, o presente trabalho teve como objetivo desenvolver um sistema web de estimativa numérica de dados meteorológicos para o Estado, com base nas estações meteorológicas automáticas de superfície, do Instituto Nacional de Meteorologia (INMET), com o intuito de dispor os dados para as regiões as quais não possuem estações. Como modelo interpolador foi utilizado o inverso da potência da distância, aplicando os expoentes de 0 a 5 e, posteriormente comparando-os através dos coeficientes de correlação, regressão e índice de desempenho. Para a realização do sistema web foram utilizadas as linguagens de programação PHP, HTML e javascript, com suporte ao banco de dados MYSQL. Utilizou-se os programas Macromedia Dreamweaver 8.0 para a programação web e HeidiSQL para gerenciar o banco de dados. Dentre as nove variáveis analisadas, apenas quatro apresentaram ótimo desempenho, sendo elas: temperatura, umidade relativa do ar, pressão atmosférica e ponto de orvalhos. O modelo de interpolação com expoente 5 foi o que apresentou melhor desempenho para as quatro variáveis. Após definição do melhor método, criou-se o banco de dados SWIM (Sistema Web de Interpolação Meteorológica) em MySQL e desenvolveu-se o sistema web, o qual ofereceu rapidez, segurança e confiabilidade para a aplicação.
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Holloway, Jacinta. "Extending decision tree methods for the analysis of remotely sensed images." Thesis, Queensland University of Technology, 2021. https://eprints.qut.edu.au/207763/1/Jacinta_Holloway_Thesis.pdf.
Full textAbstract:
One UN Sustainable Development Goal focuses on monitoring the presence, growth, and loss of forests. The cost of tracking progress towards this goal is often prohibitive. Satellite images provide an opportunity to use free data for environmental monitoring. However, these images have missing data due to cloud cover, particularly in the tropics. In this thesis I introduce fast and accurate new statistical methods to fill these data gaps. I create spatial and stochastic extensions of decision tree machine learning methods for interpolating missing data. I illustrate these methods with case studies monitoring forest cover in Australia and South America.
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Martins, Ivan Carlos Fernandes. "Insecta e Arachnida associados ao solo : plantas herbáceas como área de refúgio visando ao controle biológico conservativo /." Jaboticabal : [s.n.], 2011. http://hdl.handle.net/11449/102309.
Full textAbstract:
Orientador: Francisco Jorge CividanesBanca: Sinval Silveira Neto
Banca: Sergio Ide
Banca: José Carlos Barbosa
Banca: Antonio Carlos Busoli
Resumo: Este trabalho teve como objetivo principal avaliar o controle biológico conservativo através da criação de área de refúgio em um agroecossistema. A área de estudo foi estabelecida em um hectare com a área de refúgio apresentando 80 m de comprimento e constituída por quatro canteiros de 20 m, cada um deles contendo uma das seguintes espécies de planta herbácea perene: Panicum maximum cv. Massai e Cynodon spp. cv. Tifton 85 (gramíneas) e Stylosanthes spp. cv. BRS Campo Grande e Calopogonium mucunoides cv. Comum (leguminosas). Os artrópodes foram amostrados por meio de armadilhas de solo tipo alçapão. Todas as análises foram realizadas com as espécies consideradas predominantes classificadas de acordo com a abundância, freqüência, constância e dominância. Utilizou-se análise de regressão múltipla com seleção de variáveis "stepwise" para verificar a influência dos fatores meteorológicos na variação populacional. As fases fenológicas da soja e milho foram determinadas e relacionadas com a flutuação populacional. Para determinar a distribuição espacial os dados foram analisados através dos índices de dispersão e modelos probabilísticos. A visualização da distribuição e influência da área de refúgio foi verificada por mapa de interpolação linear. Um total de 79.633 espécimes e 514 espécies de artrópodes foram coletados. Os himenópteros e os coleópteros foram os grupos mais diversificados e abundantes, com destaque para os formicídeos e carabídeos. Os refúgios com as plantas Stylosanthes spp. e Panicum maximum apresentaram maior diversidade e abundância de artrópodes. A maioria dos artrópodes associados ao solo considerados predominantes apresentou distribuição agregada. Muitos destes, principalmente artrópodes predadores, se agruparam próximo ou na área de refúgio
Abstract: The objective of this study was to evaluate the conservation biological control through the creation of beetle bank in an agroecosystem. The study was conducted in one hectare with a 80 m long refuge area, with four blocks of 20 m., in each block one species of perennial herbaceous plant was planted: Panicum maximum cv. Masai and Cynodon spp. cv. Tifton 85 (grasses) and Stylosanthes spp. cv. BRS Campo Grande and Calopogonium mucunoides cv. Common (legumes). The Arthropods were sampled by pitfall traps. All analyses were performed with the predominant species considered classified according to the abundance, frequency, constancy, and dominance. We used multiple regression analysis with variable selection stepwise to assess the influence of meteorological factors in population. The soybean and corn phenological stages were determined and related to population fluctuation. To determine the spatial distribution, data were analyzed using dispersion indices and probabilistic models based on the frequency distribution of the arthropods. The illustration of the distribution and influence of the beetle bank was verified by linear interpolation map. A total of 79,633 specimens and 514 species of arthropods were collected. The Hymenoptera and Coleoptera were more diverse and abundant, specially ants and ground beetles. The refuges with plants Stylosanthes spp. and Panicum maximum showed greater diversity and abundance of arthropods. Aggregated distribution was showed for most predominant arthropods associated with soil. Many of these, mainly predatory arthropods, clustered near or in the beetle bank
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