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<p>Landslide susceptibility assessment involves numerous dynamic factors that can influence the predictive accuracy. This study targets Zigui County, located at the head of the Three Gorges Reservoir Area, a region prone to landslides due to its complex geological and environmental conditions. To incorporate temporal variability, the Cellular Automata-Markov (CA-Markov) model is employed to simulate and predict dynamic factors, specifically land use/land cover (LULC) changes and the normalized difference vegetation index (NDVI). The GeoDetector tool is then applied to construct an evaluation index system. Logistic regression (LR), support vector machine (SVM), and random forest (RF) models are utilized to assess landslide susceptibility, followed by a comparative analysis of their results. The results confirm the effectiveness of the CA–Markov model in predicting dynamic factors. For the 2023 land use/land cover (LULC) prediction, the proportion of cultivated land, grassland, and construction land increased by 0.49%, 0.01%, and 1.61%, respectively, while forest land and water area decreased by 1.54% and 0.56%. Additionally, the 2023 NDVI prediction, the NDVI forecast shows a 1.93% reduction in areas with positive vegetation coverage. Among the models, the RF model demonstrates higher predictive accuracy and reliability compared to the LR and SVM models. The areas with extremely high and high landslide susceptibility are mainly located along on the Yangtze River and its tributaries, including Xietan, Zhaxi, Xiangxi, Qinggan (Luogudong) and Tongzhuang Rivers, as well as along major highways such as Provincial Highway S363 and National Highway G348.</p>

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Dynamic landslide susceptibility Assessment integrating future land use and vegetation changes: Cellular-automata markov-models and machine learning for zigui county, China

  • 期刊论文

  • 发表日期：

    2025年9月15日

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  • Fei Guo,
  • Cheng Chen,
  • Hao Fang,
  • Qingshan Ma,
  • Tao Huang,
  • Hongtao Tian,
  • Qiaoyi Dai

摘要

Landslide susceptibility assessment involves numerous dynamic factors that can influence the predictive accuracy. This study targets Zigui County, located at the head of the Three Gorges Reservoir Area, a region prone to landslides due to its complex geological and environmental conditions. To incorporate temporal variability, the Cellular Automata-Markov (CA-Markov) model is employed to simulate and predict dynamic factors, specifically land use/land cover (LULC) changes and the normalized difference vegetation index (NDVI). The GeoDetector tool is then applied to construct an evaluation index system. Logistic regression (LR), support vector machine (SVM), and random forest (RF) models are utilized to assess landslide susceptibility, followed by a comparative analysis of their results. The results confirm the effectiveness of the CA–Markov model in predicting dynamic factors. For the 2023 land use/land cover (LULC) prediction, the proportion of cultivated land, grassland, and construction land increased by 0.49%, 0.01%, and 1.61%, respectively, while forest land and water area decreased by 1.54% and 0.56%. Additionally, the 2023 NDVI prediction, the NDVI forecast shows a 1.93% reduction in areas with positive vegetation coverage. Among the models, the RF model demonstrates higher predictive accuracy and reliability compared to the LR and SVM models. The areas with extremely high and high landslide susceptibility are mainly located along on the Yangtze River and its tributaries, including Xietan, Zhaxi, Xiangxi, Qinggan (Luogudong) and Tongzhuang Rivers, as well as along major highways such as Provincial Highway S363 and National Highway G348.