干旱区生态环境知识资源中心(Arid): A new real-time groundwater level forecasting strategy: Coupling hybrid data-driven models with remote sensing data

Arid

DOI	10.1016/j.jhydrol.2023.129962
	A new real-time groundwater level forecasting strategy: Coupling hybrid data-driven models with remote sensing data
	Zhang, Qixiao; Li, Peiyue; Ren, Xiaofei; Ning, Jing; Li, Jiahui; Liu, Cuishan; Wang, Yan; Wang, Guoqing
通讯作者	Wang, GQ
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2023
卷号	625
英文摘要	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 provides new insights and a reliable scientific basic for real-time groundwater level forecasting.
英文关键词	Groundwater level forecasting Arid and semi-arid region Human activity Remote sensing Wavelet transformation Shapely additive explanations
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001106774900001
WOS关键词	MOD16A2 EVAPOTRANSPIRATION PRODUCT ; MUTUAL INFORMATION ; WATER-RESOURCES ; NEURAL-NETWORK ; COMPREHENSIVE EVALUATION ; INCORRECT USAGE ; WAVELET ; PRECIPITATION ; PREDICTION ; HYDROLOGY
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/397416
推荐引用方式 GB/T 7714	Zhang, Qixiao,Li, Peiyue,Ren, Xiaofei,et al. A new real-time groundwater level forecasting strategy: Coupling hybrid data-driven models with remote sensing data[J],2023,625.
APA	Zhang, Qixiao.,Li, Peiyue.,Ren, Xiaofei.,Ning, Jing.,Li, Jiahui.,...&Wang, Guoqing.(2023).A new real-time groundwater level forecasting strategy: Coupling hybrid data-driven models with remote sensing data.JOURNAL OF HYDROLOGY,625.
MLA	Zhang, Qixiao,et al."A new real-time groundwater level forecasting strategy: Coupling hybrid data-driven models with remote sensing data".JOURNAL OF HYDROLOGY 625(2023).