Designing dynamic groundwater management strategies through a composite groundwater vulnerability model: Integrating human-related parameters into the DRASTIC model using LightGBM regression and SHAP analysis

Xu Guo; Hanxiang Xiong; Haixue Li; Xiaofan Gui; Xiaojing Hu; Yonggang Li; Hao Cui; Yang Qiu; Fawang Zhang; Chuanming Ma

doi:10.1016/j.envres.2023.116871

Designing dynamic groundwater management strategies through a composite groundwater vulnerability model: Integrating human-related parameters into the DRASTIC model using LightGBM regression and SHAP analysis

Environ Res. 2023 Nov 1;236(Pt 2):116871. doi: 10.1016/j.envres.2023.116871. Epub 2023 Aug 10.

Authors

Xu Guo¹, Hanxiang Xiong², Haixue Li³, Xiaofan Gui⁴, Xiaojing Hu², Yonggang Li², Hao Cui², Yang Qiu², Fawang Zhang⁵, Chuanming Ma⁶

Affiliations

¹ School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China. Electronic address: guoxu@cug.edu.cn.
² School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China.
³ School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China; Center for Hydrogeology and Environmental Geology Survey, China Geological Survey, Baoding, 071051, Hebei, China.
⁴ Microsoft Research, Beijing, 100000, China.
⁵ School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China; Center for Hydrogeology and Environmental Geology Survey, China Geological Survey, Baoding, 071051, Hebei, China. Electronic address: zfawang@mail.cgs.gov.cn.
⁶ School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China. Electronic address: machuanming@cug.edu.cn.

PMID: 37573023
DOI: 10.1016/j.envres.2023.116871

Abstract

Groundwater nitrate contamination has emerged as a pressing global concern. Given its potential for long-term impacts on aquifers, protective measures should primarily focus on prevention. Drawing on the theory of groundwater vulnerability (GV), the original DRASTIC model and parameters related to human activities are employed as inputs and integrated with the LightGBM regression algorithm to facilitate nitrate index (NI) prediction tasks. The SHAP analysis is conducted to effectively examine the contribution of parameters to the NI prediction and interpret the issue of parameter interactions. In addition, to mitigate the limitations of the intrinsic GV model, a composite nitrate index (CNI) is developed by linearly combining the DRASTIC index with the NI. The framework presented in this study provides adaptive strategies for managing groundwater resources over different time periods. A representative region for arid and semiarid climates, the Yinchuan region, is studied using the framework. As compared to 2012, the intrinsic GV index has changed spatially in 2022. Human activities have increased the influence of the nitrate concentration as shown by the Pearson correlation coefficient of -0.082 between the DRASTIC index and nitrate concentration. A significant increase in pollution levels was predicted by NI, ranging from -0.116 to 0.968. According to SHAP analysis, the significant increase in NI levels in 2022 was mainly due to high-value industrial and agricultural production. In 2022, 12.02% of the areas had an increase of at least 0.549 in the CNI. 42.1% of the areas were classified as moderate or high CNI levels. The farm was identified as a high-contributing source to nitrate pollution. The small-scale agricultural and livestock activities in non-urban areas also contribute to groundwater pollution. Dynamic groundwater management strategies need to be implemented in high-growth and high-level CNI areas.

Keywords: DRASTIC; Groundwater management; Groundwater vulnerability; LightGBM; Nitrate; SHAP analysis.