Combined Wavelet Transform With Long Short-Term Memory Neural Network for Water Table Depth Prediction in Baoding City, North China Plain

Accurate estimation of water table depth dynamics is essential for water resource management, especially in areas where groundwater is overexploited. In recent years, as a data-driven model, artificial neural networks (NNs) have been widely used in hydrological modeling. However, due to the non-stat...

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Auteurs principaux:	Zehua Liang, Yaping Liu, Hongchang Hu, Haoqian Li, Yuqing Ma, Mohd Yawar Ali Khan
Format:	article
Langue:	EN
Publié:	Frontiers Media S.A. 2021
Sujets:	water table depth long short-term memory neural network wavelet transform over-exploitation area feedforward neural network North China Plain Environmental sciences GE1-350
Accès en ligne:	https://doaj.org/article/e1a5258f86d142f4be6def6e4f4a910b
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Combined Wavelet Transform With Long Short-Term Memory Neural Network for Water Table Depth Prediction in Baoding City, North China Plain

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