Dimensionality reduction for groundwater forecasting under drought and intensive irrigation with neural networks

Dimensionality reduction for groundwater forecasting under drought and intensive irrigation with neural networks

Study Region: This study focuses on the Berrechid aquifer system in northern Morocco.Study Focus: The research explores Principal Component Analysis (PCA) for optimizing input selection in groundwater level forecasting using neural networks. PCA efficiently reduces input dimensionality while preserv...

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Main Authors: Tarik Bouramtane, Ismail Mohsine, Nourelhouda Karmouda, Marc Leblanc, Yannick Estève, Ilias Kacimi, Mohamed Hilali, Salima Mdhaffar, Sarah Tweed, Mounia Tahiri, Nadia Kassou, Ali El Bilali, Omar Chafki
Format: Article
Language:English
Published: Elsevier 2025-08-01
Series:Journal of Hydrology: Regional Studies
Subjects:
Principal Component Analysis (PCA)
Groundwater Level Forecasting
Semi-arid
Long short-term memory
XGBoost
Morocco
Online Access:http://www.sciencedirect.com/science/article/pii/S2214581825003027
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