Evaluating Ecological Vulnerability and Its Driving Mechanisms in the Dongting Lake Region from a Multi-Method Integrated Perspective: Based on Geodetector and Explainable Machine Learning

Fuchao Li, Tian Nan, Huang Zhang, Kun Luo, Kui Xiang and Yi Peng ()
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Fuchao Li: Changsha Natural Resources Comprehensive Investigation Center, China Geological Survey, Changsha 410600, China
Tian Nan: Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Shijiazhuang 050061, China
Huang Zhang: Changsha Natural Resources Comprehensive Investigation Center, China Geological Survey, Changsha 410600, China
Kun Luo: Changsha Natural Resources Comprehensive Investigation Center, China Geological Survey, Changsha 410600, China
Kui Xiang: Changsha Natural Resources Comprehensive Investigation Center, China Geological Survey, Changsha 410600, China
Yi Peng: Changsha Natural Resources Comprehensive Investigation Center, China Geological Survey, Changsha 410600, China

Land, 2025, vol. 14, issue 7, 1-23

Abstract: This study focuses on the Dongting Lake region in China and evaluates ecological vulnerability using the Sensitivity–Resilience–Pressure (SRP) framework, integrated with Spatial Principal Component Analysis (SPCA) to calculate the Ecological Vulnerability Index (EVI). The EVI values were classified into five levels using the Natural Breaks (Jenks) method, and spatial autocorrelation analysis was applied to reveal spatial differentiation patterns. The Geodetector model was used to analyze the driving mechanisms of natural and socioeconomic factors on EVI, identifying key influencing variables. Furthermore, the LightGBM algorithm was used for feature optimization, followed by the construction of six machine learning models—Multilayer Perceptron (MLP), Extremely Randomized Trees (ET), Decision Tree (DT), Random Forest (RF), LightGBM, and K-Nearest Neighbors (KNN)—to conduct multi-class classification of ecological vulnerability. Model performance was assessed using ROC–AUC, accuracy, recall, confusion matrix, and Kappa coefficient, and the best-performing model was interpreted using SHAP (SHapley Additive exPlanations). The results indicate that: ① ecological vulnerability increased progressively from the core wetlands and riparian corridors to the transitional zones in the surrounding hills and mountains; ② a significant spatial clustering of ecological vulnerability was observed, with a Moran’s I index of 0.78; ③ Geodetector analysis identified the interaction between NPP ( q = 0.329) and precipitation (PRE, q = 0.268) as the dominant factor ( q = 0.50) influencing spatial variation of EVI; ④ the Random Forest model achieved the best classification performance (AUC = 0.954, F 1 score = 0.78), and SHAP analysis showed that NPP and PRE made the most significant contributions to model predictions. This study proposes a multi-method integrated decision support framework for assessing ecological vulnerability in lake wetland ecosystems.

Keywords: ecological vulnerability; Dongting Lake region; SRP model; Geodetector; machine learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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