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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Vydané v:	Journal of environmental management Ročník 351; s. 119789
Hlavní autori:	Kow, Pu-Yun, Liou, Jia-Yi, Sun, Wei, Chang, Li-Chiu, Chang, Fi-John
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Elsevier Ltd 01.02.2024
Predmet:	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
On-line prístup:	Získať plný text
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