A new real-time groundwater level forecasting strategy: Coupling hybrid data-driven models with remote sensing data

• Published in: Journal of hydrology (Amsterdam) 2023-10, Vol.625, p.129962, Article 129962
• Main Authors: Zhang, Qixiao, Li, Peiyue, Ren, Xiaofei, Ning, Jing, Li, Jiahui, Liu, Cuishan, Wang, Yan, Wang, Guoqing
• Format: Article
• Language: English
• Subjects: Arid and semi-arid region; Groundwater level forecasting; Human activity; Remote sensing; Shapely additive explanations; Wavelet transformation
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2023.129962

[Display omitted] •Remote sensing dataset construction scheme to support real-time forecast is provided.•Combined the corrected WT to develop hybrid models valid for real-world application.•Hybrid model can overcome the influence of complex environments on standalone model.•The WT-LSTM model exhibits the best forecasting performance, followed by WT-XGB.•The shapely additive explanations method enhances the forecasting interpretability. Groundwater level forecasting is significantly crucial for the sustainable management of water resources, especially for arid and semi-arid regions where groundwater resources are highly dependent on. However, the complex groundwater dynamic systems in these regions are strongly influenced by climate change and human activities, which poses severe challenges to the development of accurate groundwater level forecasting models. This study first explored the selection and processing scheme of remote sensing products. Based on this, a new strategy to support real-time groundwater level forecasting with the hybrid data-driven model as the core algorithm is proposed. We applied a Deep Learning algorithm and two Ensemble Machine Learning algorithms, combined with the corrected Wavelet Transformation (WT) to develop hybrid models (WT-LSTM, WT-RF and WT-XGB) valid for real-world applications. The SHapley Additive exPlanations (SHAP) method was used to enhance the interpretability of the forecasting strategy. Real-world applications in the Xi’an and Yinchuan regions of Northwest China have shown that WT-LSTM is the hybrid model with the best overall performance with 0.843, 0.749 and 0.712 mean NSE at 1-, 2- and 3-month forecasting lead time, followed by WT-XGB with 0.763, 0.642 and 0.590 mean NSE, respectively. The accuracy of the WT-based hybrid models is significantly improved compared to the standalone model. Further analysis demonstrates that the performance of the standalone models is influenced by the local climate, especially human activities, while the corrected WT method can overcome such drawbacks. The LSTM-based models have a stronger capability than RF-based model to capture the hydrological signal affecting the local groundwater level from dataset based on remote sensing products. The SHAP method also validates the above findings and the reliability of the forecasting models. We conclude that the groundwater level forecasting strategy proposed in this study improves accuracy, interpretability and generalizability, and pro