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Relevant bibliographies by topics / Hydrologic Ensemble Prediction Systems / Dissertations / Theses

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Dissertations / Theses on the topic 'Hydrologic Ensemble Prediction Systems'

Author: Grafiati

Published: 6 September 2023

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1

Brochero, Darwin. "Hydroinformatics and diversity in hydrological ensemble prediction systems." Thesis, Université Laval, 2013. http://www.theses.ulaval.ca/2013/29908/29908.pdf.

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Nous abordons la prévision probabiliste des débits à partir de deux perspectives basées sur la complémentarité de multiples modèles hydrologiques (diversité). La première exploite une méthodologie hybride basée sur l’évaluation de plusieurs modèles hydrologiques globaux et d’outils d’apprentissage automatique pour la sélection optimale des prédicteurs, alors que la seconde fait recourt à la construction d’ensembles de réseaux de neurones en forçant la diversité. Cette thèse repose sur le concept de la diversité pour développer des méthodologies différentes autour de deux problèmes pouvant être

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2

Velazquez, Zapata Juan Alberto. "Evaluation of hydrological ensemble prediction systems for operational forecasting." Thesis, Université Laval, 2010. http://www.theses.ulaval.ca/2010/27792/27792.pdf.

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3

Velázquez, Zapata Juan Alberto. "Evaluation of hydrological ensemble prediction systems for operational forecasting." Doctoral thesis, Université Laval, 2010. http://hdl.handle.net/20.500.11794/22245.

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La prévision hydrologique consiste à évaluer quelle sera l'évolution du débit au cours des prochains pas de temps. En utilisant les systèmes actuels de prévisions hydrologiques déterministes, il est impossible d'apprécier simplement l'incertitude associée à ce type de prévision, ce que peut nuire à la prise de décisions. La prévision hydrologique d'ensemble (PHE) cherche à étayer cette incertitude en proposant, à chaque pas de temps, une distribution de probabilité, la prévision probabiliste, en place et lieu d'une estimation unique du débit, la prévision déterministe. La PHE offre de nombreux

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4

Xu, Jing. "Hydrological post-processing of streamflow forecasts issued from single-model and multimodel ensemble prediction systems." Doctoral thesis, Université Laval, 2021. http://hdl.handle.net/20.500.11794/69503.

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Les simulations et prévisions hydrologiques sont sujettes à diverses sources d'incertitudes, qui sont malheureusement inévitables. La cascade d'incertitude provient de différentes composantes de la chaîne de prévision, telles que la nature chaotique de l'atmosphère, diverses conditions initiales et limites, une modélisation hydrologique conceptuelle nécessairement inexacte et des paramètres stationnaires incohérents avec un environnement en mutation. La prévision d'ensemble s'avère un outil puissant pour représenter la croissance des erreurs dans le système dynamique et pour capter les incerti

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5

Wood, Andrew W. "Using climate model ensemble forecasts for seasonal hydrologic prediction /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/10205.

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6

Duncan, Andrew Paul. "The analysis and application of artificial neural networks for early warning systems in hydrology and the environment." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/17569.

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Artificial Neural Networks (ANNs) have been comprehensively researched, both from a computer scientific perspective and with regard to their use for predictive modelling in a wide variety of applications including hydrology and the environment. Yet their adoption for live, real-time systems remains on the whole sporadic and experimental. A plausible hypothesis is that this may be at least in part due to their treatment heretofore as “black boxes” that implicitly contain something that is unknown, or even unknowable. It is understandable that many of those responsible for delivering Early Warni

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7

Cunningham, Jeffrey G. "Applying ensemble prediction systems to Department of Defense operations." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Mar%5FCunningham.pdf.

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Thesis (M.S. in Meteorology)--Naval Postgraduate School, March 2006.<br>Thesis Advisor(s): Carlyle H. Wash, Patrick A. Harr. "March 2006." Includes bibliographical references (p. 129). Also available online.

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8

Pearman, Douglas W. "Evaluating tropical cyclone forecast track uncertainty using a grand ensemble of ensemble prediction systems." Thesis, Monterey, California. Naval Postgraduate School, 2011. http://hdl.handle.net/10945/5465.

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Approved for public release; distribution is unlimited.<br>The skill of a combined grand ensemble (GE), which is constructed from three operational global ensemble prediction systems (EPS), is evaluated with respect to the probability forecast of a tropical cyclone (TC) being within a specified area. Anisotropic probability ellipses are defined from the GE to contain 68% of the ensemble members. Forecast reliability is based on whether the forecast verifying position is within the ellipse. A sharpness parameter is based on the size of the GE-based probability ellipse relative to other oper

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9

Sağlam, Şenay Yaşar. "The role of confidence and diversity in dynamic ensemble class prediction systems." Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/1940.

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Classification is a data mining problem that arises in many real-world applications. A popular approach to tackle these classification problems is using an ensemble of classifiers that combines the collective knowledge of several classifiers. Most popular methods create a static ensemble, in which a single ensemble is constructed or chosen from a pool of classifiers and used for all new data instances. Two factors that have been frequently used to construct a static ensemble are the accuracy of and diversity among the individual classifiers. There have been many studies investigating how these

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10

Shrestha, Rajesh Raj. "River flood prediction systems : towards complementary hydrodynamic, hydrological and data driven models with uncertainty analysis /." Karlsruhe : Institut für Wasser und Gewässerentwicklung Universität Karlsruhe (TH), 2005. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=014799092&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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11

MASCARO, GIUSEPPE. "Assessing uncertainty propagation of precipitation input in hydrometeorological ensemble forecasting systems." Doctoral thesis, Università degli Studi di Cagliari, 2008. http://hdl.handle.net/11584/265979.

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The aim of the thesis is the assessment of precipitation input uncertainty into hydrological response in hydrometeorological ensemble systems for flood prediction. The study has been preliminary focused on the development of a hydrometeorological system that couples a statistical precipitation downscaling model, known as STRAIN, with a fully-distributed hydrological model, known as tRIBS. In a subsequent part of the research, a rigorous method has been designed to test the consistency hypothesis (i.e. ensemble and observations are drawn from the same distribution) of the ensemble precipitati

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12

Pincini, Giacomo. "Forecast of high-impact weather over Italy: performance of global and limited-area ensemble prediction systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16207/.

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The aim of this work is to assess the added value of the enhanced horizontal resolution in the probabilistic prediction of upper-level and surface fields. In particular, the performance of three different ensemble prediction systems were compared: ECMWF-ENS (51 members, 18 km horizontal resolution), COSMO-LEPS (16 members, 7 km horizontal resolution) and COSMO-2I-EPS (10 members, 2.2 km horizontal resolution). While the first 2 ensemble systems are operational, COSMO-2I-EPS is still in a development phase. Therefore, the intercomparison window covers a limited period, which ranges from 20 t

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13

Keller, Julia Henriette [Verfasser]. "Diagnosing the Downstream Impact of Extratropical Transition Using Multimodel Operational Ensemble Prediction Systems / Julia Henriette Keller." Karlsruhe : KIT Scientific Publishing, 2013. http://www.ksp.kit.edu.

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14

Vich, Ramis Maria del Mar. "Design of ensemble prediction systems based on potential vorticity perturbations and multiphysics. Test for western Mediterranean heavy precipitation events." Doctoral thesis, Universitat de les Illes Balears, 2012. http://hdl.handle.net/10803/84075.

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L'objectiu principal d'aquesta tesi és millorar l'actual capacitat de predicció de fenòmens meteorològics de pluja intensa potencialment perillosos a la Mediterrània occidental. Es desenvolupen i verifiquen tres sistemes de predicció per conjunts (SPC) que tenen en compte incerteses presents en els models numèrics i en les condicions inicials. Per generar els SPC s'utilitza la connexió entre les estructures de vorticitat potencial (VP) i els ciclons, a més de diferents esquemes de parametrització física. Es mostra que els SPC proporcionen una predicció més hàbil que la determinista. Els SP

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15

Keller, Julia Henriette [Verfasser], and S. [Akademischer Betreuer] Jones. "Diagnosing the Downstream Impact of Extratropical Transition Using Multimodel Operational Ensemble Prediction Systems / Julia Henriette Keller. Betreuer: S. Jones." Karlsruhe : KIT-Bibliothek, 2012. http://d-nb.info/1020230037/34.

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16

Shadabi, Fariba, and N/A. "Medical Outcome Prediction: A Hybrid Artificial Neural Networks Approach." University of Canberra. Information Sciences & Engineering, 2007. http://erl.canberra.edu.au./public/adt-AUC20070816.130444.

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This thesis advances the understanding of the application of artificial neural networks ensemble to clinical data by addressing the following fundamental question: What is the potentiality of an ensemble of neural networks models as a filter and classifier in a complex clinical situation? A novel neural networks ensemble classification model called Rules and Information Driven by Consistency in Artificial Neural Networks Ensemble (RIDCANNE) is developed for the purpose of prediction of medical outcomes or events, such as kidney transplants. The proposed classification model is based on combina

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17

Li, Jianeng. "Research on a Heart Disease Prediction Model Based on the Stacking Principle." Thesis, Högskolan Dalarna, Informatik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:du-34591.

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In this study, the prediction model based on the Stacking principle is called the Stacking fusion model. Little evidence demonstrates that the Stacking fusion model possesses better prediction performance in the field of heart disease diagnosis than other classification models. Since this model belongs to the family of ensemble learning models, which has a bad interpretability, it should be used with caution in medical diagnoses. The purpose of this study is to verify whether the Stacking fusion model has better prediction performance than stand-alone machine learning models and other ensemble

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18

Gogonel, Adriana Geanina. "Statistical Post-Processing Methods And Their Implementation On The Ensemble Prediction Systems For Forecasting Temperature In The Use Of The French Electric Consumption." Phd thesis, Université René Descartes - Paris V, 2012. http://tel.archives-ouvertes.fr/tel-00798576.

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The thesis has for objective to study new statistical methods to correct temperature predictionsthat may be implemented on the ensemble prediction system (EPS) of Meteo France so toimprove its use for the electric system management, at EDF France. The EPS of Meteo Francewe are working on contains 51 members (forecasts by time-step) and gives the temperaturepredictions for 14 days. The thesis contains three parts: in the first one we present the EPSand we implement two statistical methods improving the accuracy or the spread of the EPS andwe introduce criteria for comparing results. In the seco

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19

Roulin, Emmannuel. "Medium-range probabilistic river streamflow predictions." Doctoral thesis, Universite Libre de Bruxelles, 2014. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209270.

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River streamflow forecasting is traditionally based on real-time measurements of rainfall over catchments and discharge at the outlet and upstream. These data are processed in mathematical models of varying complexity and allow to obtain accurate predictions for short times. In order to extend the forecast horizon to a few days - to be able to issue early warning - it is necessary to take into account the weather forecasts. However, the latter display the property of sensitivity to initial conditions, and for appropriate risk management, forecasts should therefore be considered in probabilisti

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20

Steinberg, Rebecca M. "Predicting Post-Mining Hydrologic Effects of Underground Coal Mines in Ohio throughMultivariate Statistical Analyses and GIS Tool Building." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1555429886192267.

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21

Young, Roland Michael Brendon. "Predictability of a laboratory analogue for planetary atmospheres." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:b4f483a6-437c-4914-b94e-cb04d996b337.

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The thermally-driven rotating annulus is a laboratory experiment used to study the dynamics of planetary atmospheres under controlled and reproducible conditions. The predictability of this experiment is studied by applying the same principles used to predict the atmosphere. A forecasting system for the annulus is built using the analysis correction method for data assimilation and the breeding method for ensemble generation. The results show that a range of flow regimes with varying complexity can be accurately assimilated, predicted, and studied in this experiment. This framework is also int

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22

Sarath, M. "At-site and Multisite Probabilistic Forecasting of Streamflow." Thesis, 2019. https://etd.iisc.ac.in/handle/2005/5126.

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Streamflow forecasts are very useful for a variety of applications such as flood warning, reservoir operation and water resources planning and management, especially in countries like India where streamflow can be highly variable. Methods available for streamflow forecasting can be broadly classified as process-driven and data-driven methods. Forecasts always have uncertainty associated with them due to limitations in modelling complex processes in the hydrologic system, and factors such as scarcity of data and measurement errors. It is important to quantify the forecast uncertainty for making

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23

Lin, Shu-Chen, and 林淑真. "Fractals and Chaos on the Analysis and Prediction of Nonlinear Hydrologic Systems." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/65671694228916805369.

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博士<br>國立成功大學<br>水利及海洋工程學系<br>87<br>The fractal geometry and the chaotic dynamics are two important components in nonlinear sciences. They are powerful tools to analyze the complex hydrologic systems currently. In this study, we focus the attention on hydrosciences. Eight topics are concerned and described as follows: (1) estimation of point-fractal dimension and its variability for hydrologic process, (2) analysis and clustering for areal fractal characteristics, (3) identification of time-scale law and fractal approach of frequency analysis for hydrologic variable, (4) existence identificatio

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