A groundwater level spatiotemporal prediction model based on graph convolutional networks with a long short-term memory
发布时间：2024-11-06
点击次数：
影响因子：2.7
DOI码：10.2166/hydro.2024.226
发表刊物：Journal of Hydroinformatics
摘要：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.
合写作者：Z. Jiang, L. Song, X. Yu, S. Yuan
第一作者：L. Wang
论文类型：期刊论文
通讯作者：B. Zhang
论文编号：226
学科门类：工学
一级学科：地质资源与地质工程
文献类型：J
页面范围：226
是否译文：否
发表时间：2024-11-05
收录刊物：SCI