Watershed groundwater level multistep ahead forecasts by fusing convolutional-based autoencoder and LSTM models

The development of deep learning-based groundwater level forecast models can tackle the challenge of high dimensional groundwater dynamics, predict groundwater variation trends accurately, and manage groundwater resources effectively, thereby contributing to sustainable water resources management. T...

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Veröffentlicht in:	Journal of environmental management Jg. 351; S. 119789
Hauptverfasser:	Kow, Pu-Yun, Liou, Jia-Yi, Sun, Wei, Chang, Li-Chiu, Chang, Fi-John
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England Elsevier Ltd 01.02.2024
Schlagworte:	case studies Convolutional neural network (CNN) data collection Deep learning environmental management groundwater Groundwater level forecast HBV-Light model Long short-term memory neural network (LSTM) neural networks Taiwan water management water table watersheds Taiwan Deep learning Long short-term memory neural network (LSTM) Convolutional neural network (CNN) Groundwater level forecast HBV-Light model
ISSN:	0301-4797, 1095-8630, 1095-8630
Online-Zugang:	Volltext
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