Journal of Geo-information Science ›› 2023, Vol. 25 ›› Issue (5): 953-966.doi: 10.12082/dqxxkx.2023.220567

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Landslide Susceptibility Assessment Method Considering Land Use Dynamic Change

LIN Xuanxin1,2(), XIAO Guirong1,2,*(), ZHOU Houbo3,4   

  1. 1. The Academy of Digital China(Fujian), Fuzhou University, Fuzhou 350108, China
    2. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350108, China
    3. Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
    4. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2022-08-03 Revised:2022-11-15 Online:2023-05-25 Published:2023-04-27
  • Contact: XIAO Guirong;
  • Supported by:
    The Central Guided Local Development of Science and Technology Project(2020L3005);The Strategic Priority Research Program of the Chinese Academy of Sciences(Class A)(XDA23100504)


The causes of landslide disasters are complex, and landslide susceptibility assessment is of great significance for disaster warning, prevention, and control management. In the previous mapping studies on landslide susceptibility assessment, land use change factor was not considered. This paper proposed a combination of factors for landslide susceptibility assessment by considering land use dynamic change factor. The landslide frequency ratio was used to quantitatively measure the correlation between land use change and landslide development. And Logistic Regression (LR) model was used to compare the prediction ability of the model before and after the introduction of land use change factor. We constructed three machine learning models: Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), and Random Forest (RF). We used AUC and other indicators to compare model performance. Finally, we took Sanming City of Fujian Province as the study area and the whole Fujian Province as the verification area to conduct the landslide susceptibility assessment research. The results show that there is a strong correlation between land use change factor and landslide development. The inclusion of land use change factor improves model prediction accuracy, which indicates that it is necessary to introduce dynamic factor in the assessment of landslide susceptibility. The verification results show that RF model has higher prediction accuracy than DT and GBDT. The high landslide prone areas are mainly distributed in the west and central of Sanming City, where the land use change degree is high, and the impact of human activities is great. The low landslide prone areas basically locate in the high-altitude areas with little influence of human activities. This study provides a new research perspective for landslide susceptibility assessment and helps to explore the impact of human activities on disaster formation.

Key words: landslide, susceptibility, land use change, ensemble learning, decision tree, gradient boosting decision tree, random forest, Sanming