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

doi:10.1016/j.jenvman.2023.119789

淡江大學機構典藏 > 工學院 > 人工智慧學系 > 期刊論文 > Item 987654321/125500

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题名:	Watershed groundwater level multistep ahead forecasts by fusing convolutional-based autoencoder and LSTM models
作者:	Kow, Pu-Yun;Liou, Jia-Yi;Sun, Wei;Chang, Li-Chiu;Chang, Fi-John
日期:	2023-12-14
上传时间:	2024-07-05 12:05:31 (UTC+8)
出版者:	Elsevier
摘要:	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. This study proposed a novel ConvAE-LSTM model, which fused a Convolutional-based Autoencoder model (ConvAE) and a Long Short-Term Memory Neural Network model (LSTM), to provide accurate spatiotemporal groundwater level forecasts over the next three months. The HBV-light and LSTM models are chosen as benchmarks. An ensemble of point data and the corresponding derived images concerning the past (observations) and the future (forecasts from a conceptual model) of groundwater levels at 33 groundwater wells in Jhuoshuei River basin of Taiwan between 2000 and 2019 constituted the case study. The findings showcase the effectiveness of the ConvAE-LSTM model in extracting crucial features from both point and imagery datasets. This model successfully establishes spatiotemporal dependencies between regional images and groundwater level data over diverse time frames, leading to accurate multi-step-ahead forecasts of groundwater levels. Notably, the ConvAE-LSTM model exhibits a substantial improvement, with the R-squared values showing an increase of more than 18%, 22%, and 49% for the R1, R2, and R3 regions, respectively, compared to the HBV-light model. Additionally, it outperforms the LSTM model in this regard. This study represents a noteworthy milestone in environmental modeling, offering key insights for designing sustainable groundwater management strategies to ensure the long-term availability of this vital resource.
關聯:	Journal of Environmental Management, 351, 119789
DOI:	10.1016/j.jenvman.2023.119789
显示于类别:	[人工智慧學系] 期刊論文

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