A groundwater level spatiotemporal prediction model based on graph convolutional networks with a long short-term memory
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Release time:2024-11-06 Impact Factor:2.7 DOI number:10.2166/hydro.2024.226 Journal:Journal of Hydroinformatics Abstract:The performance of regional groundwater level (GWL) prediction model hinges on understanding intricate spatiotemporal correlations among monitoring wells. In this study, a graph convolutional network (GCN) with a long short-term memory (LSTM) (GCN–LSTM) model is introduced for GWL prediction utilizing data from 16 wells located in the northeastern Xiangtan City, China. This model is designed to account for both the hybrid temporal dependencies and spatial autocorrelations among wells. It consists of two parts: the spatial part employs GCNs to extract spatial characteristics from a spatial self-similarity weight matrix and an attribute self-similarity wight matrix among wells; the temporal part utilizes a LSTM module to capture the temporal patterns of GWL sequences, along with monthly precipitation and temperature data. This model dynamically predicts changes in groundwater levels, achieving higher accuracy on average compared to single-well predictions using LSTM. By incorporating both temporal dependencies and spatial autocorrelations, the GCN–LSTM model demonstrated an average improvement in goodness-of-fit of approximately 11.21% over the LSTM-based model for individual wells. Its application holds significant reference value for the sustainable utilization and development of groundwater resources in Xiangtan City. Co-author:Z. Jiang, L. Song, X. Yu, S. Yuan First Author:L. Wang Indexed by:Journal paper Correspondence Author:B. Zhang Document Code:226 Discipline:Engineering First-Level Discipline:Geological Resources and Geological Engineering Document Type:J Page Number:226 Translation or Not:no Date of Publication:2024-11-05 Included Journals:SCI Links to published journals:https://doi.org/10.2166/hydro.2024.226 -
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