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Rainfall-runoff modeling using deep learning application to mediterranean climate / Rania Mokhtari (2022)

Rainfall-runoff modeling using deep learning application to mediterranean climate [document électronique] / Rania Mokhtari, Auteur ; Maria Ameddah, Auteur ; Abdelmalek Bermad, Directeur de thèse ; Rafik Oulebsir, Directeur de thèse . - [S.l.] : [s.n.], 2022 . - 1 fichier PDF (5.24 MO).
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Hydraulique : Alger, École Nationale Polytechnique : 2022.
Bibliogr. f. 108-113. - Appendix
Langues : Anglais (eng)
Mots-clés : Modeling
Hydrological
Deep Learning
LSTM
RNN
Index. décimale : PH00322
Résumé :
Rainfall-runoff modeling is an important tool for water resources management in watersheds and hydrological hazard predictions such as floods. Several research has been carried out by hydrologists to produce efficient models that generate the watersheds’ responses to precipitation. Generally, these models involve parameters that are often unavailable, and even difficult to measure. Therefore, it may be practical to focus on new Deep Learning methods, which are powerful tools that can understand the complexity of the non-linearity relationship between inputs and outputs without having to resort to several parameters.
In this study, the authors used two different models RNN and LSTM on daily data from 5 catchments with a Mediterranean climate where the LSTM model showed better results for what was evaluated by the NSE. Other assessments were made on the LSTM model by RSR and PBIAS where the precipitation and antecedent flow being the parameters that most influenced the model.

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Mots-clés :	Modeling Hydrological Deep Learning LSTM RNN
Index. décimale :	PH00322
Résumé :	Rainfall-runoff modeling is an important tool for water resources management in watersheds and hydrological hazard predictions such as floods. Several research has been carried out by hydrologists to produce efficient models that generate the watersheds’ responses to precipitation. Generally, these models involve parameters that are often unavailable, and even difficult to measure. Therefore, it may be practical to focus on new Deep Learning methods, which are powerful tools that can understand the complexity of the non-linearity relationship between inputs and outputs without having to resort to several parameters. In this study, the authors used two different models RNN and LSTM on daily data from 5 catchments with a Mediterranean climate where the LSTM model showed better results for what was evaluated by the NSE. Other assessments were made on the LSTM model by RSR and PBIAS where the precipitation and antecedent flow being the parameters that most influenced the model.