Contribution à la prédiction des précipitations par l'apprentissage automatique (machine learning)

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DC Field	Value	Language
dc.contributor.author	Belasla, Yasmine	-
dc.date.accessioned	2025-02-24T08:37:39Z	-
dc.date.available	2025-02-24T08:37:39Z	-
dc.date.issued	2024	-
dc.identifier.uri	http://localhost:8080/xmlui/handle/123456789/2165	-
dc.description	Master	en_US
dc.description.abstract	The evolution of climate conditions and their impact on water resources present new challenges for the management of hydraulic infrastructures, especially wastewater networks. In this context, precipitation forecasting becomes crucial. This study explores the application of Long Short-Term Memory (LSTM) neural networks to predict rainfall over a period of 120 months. The goal is to enhance the anticipation of extreme rainfall events and improve the planning of sewage systems. The results demonstrate the effectiveness of the LSTM model in predicting hydrological time series with a good fit between real and predicted values	en_US
dc.language.iso	fr	en_US
dc.subject	Precipitation, Prediction, LSTM neural networks, Time series, Water resources, Sanitation networks	en_US
dc.title	Contribution à la prédiction des précipitations par l'apprentissage automatique (machine learning)	en_US
dc.type	Thesis	en_US
Appears in Collections:	Conception des Systèmes d'Assainissement

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