Relevant bibliographies by topics / Semivariogram models

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Academic literature on the topic 'Semivariogram models'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Semivariogram models"

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Mohd Aziz, Mohd Khairul Bazli, Fadhilah Yusof, Zalina Mohd Daud, Zulkifli Yusop, and Mohammad Afif Kasno. "Comparison of Semivariogram Models in Rain Gauge Network Design." MATEMATIKA 35, no. 2 (2019): 157–70. http://dx.doi.org/10.11113/matematika.v35.n2.1155.

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The well-known geostatistics method (variance-reduction method) is commonly used to determine the optimal rain gauge network. The main problem in geostatistics method to determine the best semivariogram model in order to be used in estimating the variance. An optimal choice of the semivariogram model is an important point for a good data evaluation process. Three different semivariogram models which are Spherical, Gaussian and Exponential are used and their performances are compared in this study. Cross validation technique is applied to compute the errors of the semivariograms. Rain-fall data
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Batistella Pasini, Mauricio Paulo, Alessandro Dal'Col Lúcio, and Alberto Filho Cargnelutti. "Semivariogram models for estimating fig fly population density throughout the year." Pesquisa Agropecuária Brasileira 49, no. 7 (2014): 493–505. http://dx.doi.org/10.1590/s0100-204x2014000700001.

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The objective of this work was to select semivariogram models to estimate the population density of fig fly (Zaprionus indianus; Diptera: Drosophilidae) throughout the year, using ordinary kriging. Nineteen monitoring sites were demarcated in an area of 8,200 m2, cropped with six fruit tree species: persimmon, citrus, fig, guava, apple, and peach. During a 24 month period, 106 weekly evaluations were done in these sites. The average number of adult fig flies captured weekly per trap, during each month, was subjected to the circular, spherical, pentaspherical, exponential, Gaussian, rational qu
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Xavier Júnior, Sílvio Fernando Alves, Jader da Silva Jale, Tatijana Stosic, Carlos Antonio Costa dos Santos, and Vijay P. Singh. "Precipitation trends analysis by Mann-Kendall test: a case study of Paraíba, Brazil." Revista Brasileira de Meteorologia 35, no. 2 (2020): 187–96. http://dx.doi.org/10.1590/0102-7786351013.

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Abstract This work aimed to select semivariogram models to estimate trends in monthly precipitation in Paraiba State-Brazil using ordinary kriging. The methodology involves the application of geostatistical interpolation of precipitation records of 51 years from 69 rainfall stations across the state. Analysis of semivariograms showed that specific months had a strong spatial dependence (Index of Spatial Dependence - IDE < 25%). The trends were subjected to the following models: circular, spherical, pentaspherical, exponential, Gaussian, rational quadratic, K-Bessel and tetraspherical. The b
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Pyrcz, Michael J., and Clayton V. Deutsch. "Spectral Corrected Semivariogram Models." Mathematical Geology 38, no. 7 (2007): 891–99. http://dx.doi.org/10.1007/s11004-006-9053-9.

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Moonchai, Sompop, and Nawinda Chutsagulprom. "Semiparametric Semivariogram Modeling with a Scaling Criterion for Node Spacing: A Case Study of Solar Radiation Distribution in Thailand." Mathematics 8, no. 12 (2020): 2173. http://dx.doi.org/10.3390/math8122173.

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Geostatistical interpolation methods, sometimes referred to as kriging, have been proven effective and efficient for the estimation of target quantity at ungauged sites. The merit of the kriging approach relies heavily on the semivariograms in which the parametric functions are prevalently used. In this work, we explore the semiparametric semivariogram where no close-form semivariogram is required. By additionally enforcing the monotonicity condition in order to suppress the presence of spurious oscillation, a scaling of the nodes of the semiparametric kriging is proposed. To this end, the sol
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Vieira, Sidney Rosa, José Ruy Porto de Carvalho, and Antonio Paz González. "Jack knifing for semivariogram validation." Bragantia 69, suppl (2010): 97–105. http://dx.doi.org/10.1590/s0006-87052010000500011.

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The semivariogram function fitting is the most important aspect of geostatistics and because of this the model chosen must be validated. Jack knifing may be one the most efficient ways for this validation purpose. The objective of this study was to show the use of the jack knifing technique to validate geostatistical hypothesis and semivariogram models. For that purpose, topographical heights data obtained from six distinct field scales and sampling densities were analyzed. Because the topographical data showed very strong trend for all fields as it was verified by the absence of a sill in the
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Paz-Ferreiro, Jorge, Eva Vidal Vázquez, and Sidney Rosa Vieira. "Geostatistical analysis of a geochemical dataset." Bragantia 69, suppl (2010): 121–29. http://dx.doi.org/10.1590/s0006-87052010000500013.

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The application of geostatistics to data obtained from geochemical prospecting process can provide useful information for evaluating mineralization potential. The objective of this study was to evaluate the spatial distribution of Au, As and Sb contents over a large area of the Coruña province, Spain. A geochemical survey was carried out from which a data set with 323 samples was collected. Macroelements and trace elements were determined by routine analytical techniques. The spatial variability was assessed using semivariogram and cross-semivariogram as well as indicator semivariogram analysi
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Obroślak, Radomir, and Oleksandr Dorozhynskyy. "Selection of a semivariogram model in the study of spatial distribution of soil moisture." Journal of Water and Land Development 35, no. 1 (2017): 161–66. http://dx.doi.org/10.1515/jwld-2017-0080.

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AbstractThe paper presents a selection of a semivariogram model in the study of spatial variability of soil moisture in a loess agricultural catchment. Soil moisture tests were carried out in the Moszenki village, 15 km northwest of Lublin. Soil moisture measurements were performed at two dates at 104 points, located on a rectangular surface measuring 700 × 1200 m. These points were laid out in the corners of a grid of squares with sides 100 m. In addition, 6 measurements were made at a distance of less than 100 m from the nearest points. Soil moisture was measured in the soil surface (0–5 cm)
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Wu, S. J., P. H. Chen, and J. C. Yang. "Application of Weighted Semivariogram Model (WSVM) based on fitness to experimental semivariogram on estimation of rainfall amount." Hydrology and Earth System Sciences Discussions 8, no. 2 (2011): 4229–59. http://dx.doi.org/10.5194/hessd-8-4229-2011.

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Abstract. This study presents a weighted semivariogram model (WSVM) which is intended to reduce the uncertainties in the selection of the best-fit semivariogram model and associated parameters. The proposed WSVM is based on the combined forecast method, providing the weighted average semivariogram by summing up the product of estimated semivariograms and weighted factors, which are related with the inverse of the objective function value associated with the optimal parameters of theoretical semivariogram models (TSVMs). A WSVM can save computation time in the estimation of rainfall amount with
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Sen, Zek�i. "Cumulative semivariogram models of regionalized variables." Mathematical Geology 21, no. 8 (1989): 891–903. http://dx.doi.org/10.1007/bf00894454.

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Dissertations / Theses on the topic "Semivariogram models"

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Huang, Fang. "Modeling patterns of small scale spatial variation in soil." Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-011106-155345/.

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Thesis (M.S.)--Worcester Polytechnic Institute.<br>Keywords: spatial variations; nested random effects models; semivariogram models; kriging methods; multiple logistic regression models; missing; multiple imputation. Includes bibliographical references (p. 35-36).
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Batista, Flávia Ferreira. "Influência de modelos de dependência espacial na definição de mapas temáticos." Universidade Federal de Viçosa, 2012. http://locus.ufv.br/handle/123456789/4059.

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Made available in DSpace on 2015-03-26T13:32:16Z (GMT). No. of bitstreams: 1 texto completo.pdf: 862787 bytes, checksum: b23a777918d630895ac6bf3d64e985c8 (MD5) Previous issue date: 2012-07-24<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>In recent years Geostatistics has been widely used in the field of precision agriculture, this is due to being a tool to analyze the spatial variability exists in the area of agricultural production, enabling assess the need for creation of subareas or zones where they are handlings performed differently. One focus of precision agricultur
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Marins, Araceli Ciotti de. "Rendimento de grãos e de óleo do crambe em um latossolo: Análise espacial e modelos de corrlação." Universidade Federal de Santa Maria, 2014. http://repositorio.ufsm.br/handle/1/3359.

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The growing environmental awareness regarding production and use of renewable fuels has led many of countries to create policies to benefit producers of renewable fuels. Thus, the search for raw materials for production of biofuels, which do not conflict with the global food production and exhibit similar performance to fossil fuels, has generated interest towards crambe, a crop of high oil content, inappropriate for animal consumption and which can be applied in crop rotation without the need for exchange farm machinery. However, studies evaluating the influence of spatial variability of soil
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Cantu, Jacqueline Gabriela. "Influência de configurações amostrais na qualidade de estimação espacial sob o uso de modelos espaciais bivariados." Universidade Estadual do Oeste do Parana, 2015. http://tede.unioeste.br:8080/tede/handle/tede/203.

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Made available in DSpace on 2017-05-12T14:47:07Z (GMT). No. of bitstreams: 1 protegidoJacqueline_dissertacao.pdf: 3012964 bytes, checksum: 337f371fa8c665bb4fdfdc22709938da (MD5) Previous issue date: 2015-02-02<br>The soil spatial variability s studies are based in geostatistics which appears as a method whose data comes from natural phenomena and consider the geographical location of the phenomenon. If in an area under study the researcher has interest in searching the variability of variables and has evidence that the steps which describe the spatial structure of this variables aren t indep
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Ferreira, Matheus de Paula. "Redução do adensamento amostral no ajuste de modelos de semivariogramas." Universidade Federal de Viçosa, 2015. http://www.locus.ufv.br/handle/123456789/7171.

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Submitted by Reginaldo Soares de Freitas (reginaldo.freitas@ufv.br) on 2016-01-12T15:03:46Z No. of bitstreams: 1 texto completo.pdf: 1371110 bytes, checksum: 581fed4ae8bb926e72283face6e16e2c (MD5)<br>Made available in DSpace on 2016-01-12T15:03:46Z (GMT). No. of bitstreams: 1 texto completo.pdf: 1371110 bytes, checksum: 581fed4ae8bb926e72283face6e16e2c (MD5) Previous issue date: 2015-07-30<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>A Geoestatística é o ramo da estatística que visa descrever o comportamento espacial dos dados, analisando as variáveis de acordo com a
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Campos, Josmar Furtado de. "Eficiência da análise estatística espacial na classificação de famílias do feijoeiro - estudo via simulação." Universidade Federal de Viçosa, 2011. http://locus.ufv.br/handle/123456789/4039.

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Made available in DSpace on 2015-03-26T13:32:11Z (GMT). No. of bitstreams: 1 texto completo.pdf: 446864 bytes, checksum: 1ba8efc18a08b922adc7e2c5eb3dc55c (MD5) Previous issue date: 2011-02-24<br>The aim of this study was to evaluate the efficiency of spatial analysis, which considers spatially dependent errors, for classification of common bean families in relation to traditional analysis in randomized blocks and lattice that assuming independent errors. Were considered different degrees of spatial dependence and experimental precision. Were taken as reference to simulate the results of seve
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Watanabe, Jorge. "Métodos geoestatísticos de co-estimativas: estudo do efeito da correlação entre variáveis na precisão dos resultados." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/44/44137/tde-14082008-165227/.

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Esta dissertação de mestrado apresenta os resultados de uma investigação sobre os métodos de co-estimativa comumente utilizados em geoestatística. Estes métodos são: cokrigagem ordinária; cokrigagem colocalizada e krigagem com deriva externa. Além disso, a krigagem ordinária foi considerada apenas a título de ilustração como esse método trabalha quando a variável primária estiver pobremente amostrada. Como sabemos, os métodos de co-estimativa dependem de uma variável secundária amostrada sobre o domínio a ser estimado. Adicionalmente, esta variável deveria apresentar correlação linear com a va
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HO, Shin-Li, and 何信霖. "The Feasibility of Using Artificial Neural Network as an Alternative to Semivariogram Model in Kriging Method – Verified with Soil Heavy Metal Data." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/17298405659718857640.

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碩士<br>朝陽科技大學<br>環境工程與管理系碩士班<br>97<br>Spatial interpolation has become one of the requisite tools in Earth''s surface science. Due to the feature of unbias and minimized variance of predicted error, Kriging method was widely used among various interpolation methods. However, because the semivariogram model in Kriging methods is a parametric model, user need to try and error to find the best semivariogram model to yield sound results. Such process brings inconvenience for Kriging user. On the other hand, artificial neural network is a non-parametric model, that is independent to the structure of
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Book chapters on the topic "Semivariogram models"

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Elprince, Adel M. "Spatial Analysis Using a Proportional Effect Semivariogram Model." In Developments in Soil Salinity Assessment and Reclamation. Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5684-7_12.

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Samui, Pijush, Viswanathan R., Jagan J., and Pradeep U. Kurup. "Determination of Spatial Variability of Rock Depth of Chennai." In Advances in Computational Intelligence and Robotics. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2857-9.ch023.

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This study adopts four modeling techniques Ordinary Kriging(OK), Generalized Regression Neural Network (GRNN), Genetic Programming(GP) and Minimax Probability Machine Regression(MPMR) for prediction of rock depth(d) at Chennai(India). Latitude (Lx) and Longitude(Ly) have been used as inputs of the models. A semivariogram has been constructed for developing the OK model. The developed GP gives equation for prediction of d at any point in Chennai. A comparison of four modeling techniques has been carried out. The performance of MPMR is slightly better than the other models. The developed models give the spatial variability of rock depth at Chennai.
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Samui, Pijush, Viswanathan R., Jagan J., and Pradeep U. Kurup. "Determination of Spatial Variability of Rock Depth of Chennai." In Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-8048-6.ch006.

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This study adopts four modeling techniques Ordinary Kriging(OK), Generalized Regression Neural Network (GRNN), Genetic Programming(GP) and Minimax Probability Machine Regression(MPMR) for prediction of rock depth(d) at Chennai(India). Latitude (Lx) and Longitude(Ly) have been used as inputs of the models. A semivariogram has been constructed for developing the OK model. The developed GP gives equation for prediction of d at any point in Chennai. A comparison of four modeling techniques has been carried out. The performance of MPMR is slightly better than the other models. The developed models give the spatial variability of rock depth at Chennai.
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Biswas, Asim, and Bing Cheng. "Model Averaging for Semivariogram Model Parameters." In Advances in Agrophysical Research. InTech, 2013. http://dx.doi.org/10.5772/52339.

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"Spatio-temporal structural analysis (I): empirical semivariogram and covariogram estimation and model fitting." In Spatial and Spatio-Temporal Geostatistical Modeling and Kriging. John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118762387.ch6.

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Conference papers on the topic "Semivariogram models"

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Sari, Rr Kurnia Novita, and Udjianna S. Pasaribu. "The comparison of isotropic and anisotropic semivariogram for Gauss model." In 4TH INTERNATIONAL CONFERENCE ON MATHEMATICS AND NATURAL SCIENCES (ICMNS 2012): Science for Health, Food and Sustainable Energy. AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4868855.

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Madu, Osita Robinson, Jerry Orrelo Athoja, Amarachi Queen Kalu, and Obi Mike Onyekonwu. "Integrated Approach to Geostatistics For Optimal Reservoir Properties Distribution – Case Study of X-Reservoir in Niger Delta Basin." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/207116-ms.

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Abstract In-depth knowledge of geostatistical analysis, environment of deposition and reservoir facies types is important for optimal distribution of reservoir properties across the reservoir grid. Geostatistics is a veritable tool that is quantitatively used to model spatial continuity, anisotropy direction and capture reservoir heterogeneity for optimal distribution of reservoir properties. When spatial continuity and heterogeneity level of the reservoir are adequately understood and modeled, representative property distribution becomes possible. In the face of limited well data, modeling ma
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Faisal, Fachri, Pepi Novianti, and Siska Yosmar. "Ordinary Kriging Method using Isotropic Semivariogram Model for Estimating the Earthquake Strength in Bengkulu Province." In International Conference on Mathematics and Islam. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0008516800340040.

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McKeand, Austin M., Recep M. Gorguluarslan, and Seung-Kyum Choi. "A Stochastic Approach for Performance Prediction of Aircraft Engine Components Under Manufacturing Uncertainty." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85415.

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Efficient modeling of uncertainty introduced by the manufacturing process is critical in the design of the components of the aircraft engines. In this study, a stochastic approach is presented to efficiently account for the geometric uncertainty, associated with the manufacturing process, in the accurate performance prediction of aircraft engine components. A semivariogram analysis procedure is proposed in this approach to quantify spatial variability of the uncertain geometric parameters based on the manufactured specimens. Karhunen-Loeve expansion is utilized to create a set of correlated ra
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