Relevant bibliographies by topics / Hydrologic Ensemble Prediction Systems

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Academic literature on the topic 'Hydrologic Ensemble Prediction Systems'

Author: Grafiati
Published: 6 September 2023

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

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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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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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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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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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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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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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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