Analysis of Water Source Conservation Driving Factors Based on Machine Learning

Yixuan Jia, Zhe Zhang, Chunhua Huang () and Shuibo Xie ()
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Yixuan Jia: Department of Urban and Rural Planning, Solux College of Architecture and Design Arts, University of South China, Hengyang 421001, China
Zhe Zhang: Department of Urban and Rural Planning, Solux College of Architecture and Design Arts, University of South China, Hengyang 421001, China
Chunhua Huang: Hunan Provincial Engineering Research Center for Healthy City Construction, Key Laboratory of Eco-Regional Urban Planning and Management in Hengyang, Department of Urban and Rural Planning, Songlin College of Architecture and Design Arts, University of South China, Hengyang 421001, China
Shuibo Xie: Key Discipline Laboratory for National Defense of Biotechnology in Uranium Mining and Hydrometallurgy, University of South China, Hengyang 421001, China

Sustainability, 2025, vol. 17, issue 4, 1-22

Abstract: This study focuses on the spatiotemporal dynamic changes in water retention capacity and the nonlinear research of its influencing factors. By using the InVEST model, the changing trends of water retention capacity in different regions and at various time scales were analyzed. Based on this, the results were further examined using the CatBoost model with SHAP (SHapley Additive exPlanations) analysis and PDP (Partial Dependence Plot) analysis. The results show the following: (1) From 2003 to 2023, the water conservation capacity first increased and then decreased, and spatially, the water conservation capacity of the mountainous area in the west of the Yiluo River Basin and Xionger Mountain in the middle part of the basin increased as a whole. At the same time, the forest land in the basin contributed more than 60% of the water conservation capacity. (2) Precipitation is the most significant driving factor for water conservation in the basin, and plant water content, soil type, and temperature are also the main driving factors for water conservation in the Yiluo River Basin. (3) The interaction between temperature and other influencing factors can significantly improve water conservation. This research not only provides scientific evidence for understanding the driving mechanisms of water conservation but also offers references for water resource management and ecological protection planning.

Keywords: machine learning methods; water conservation prediction; impact factor assessment; SHAP analysis; PDP analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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