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Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County, Three Gorges Reservoir, China

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Abstract

Landslide susceptibility mapping is vital for landslide risk management and urban planning. In this study, we used three statistical models [frequency ratio, certainty factor and index of entropy (IOE)] and a machine learning model [random forest (RF)] for landslide susceptibility mapping in Wanzhou County, China. First, a landslide inventory map was prepared using earlier geotechnical investigation reports, aerial images, and field surveys. Then, the redundant factors were excluded from the initial fourteen landslide causal factors via factor correlation analysis. To determine the most effective causal factors, landslide susceptibility evaluations were performed based on four cases with different combinations of factors (“cases”). In the analysis, 465 (70%) landslide locations were randomly selected for model training, and 200 (30%) landslide locations were selected for verification. The results showed that case 3 produced the best performance for the statistical models and that case 2 produced the best performance for the RF model. Finally, the receiver operating characteristic (ROC) curve was used to verify the accuracy of each model’s results for its respective optimal case. The ROC curve analysis showed that the machine learning model performed better than the other three models, and among the three statistical models, the IOE model with weight coefficients was superior.

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Acknowledgements

This paper was prepared as part of the projects “The risk assessment of geological hazards induced by reservoir water level fluctuation in Chongqing, Three-Gorges Reservoir, China.” (No. 2016065135) and “The study of mechanism and forecast criterion of the gentle-dip landslides in The Three Gorges Reservoir Region, China” (No. 41572292) funded by the National Natural Science Foundation of China.

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Authors and Affiliations

  1. Faculty of Engineering, China University of Geosciences, Wuhan, 430074, Hubei, China

    Ting Xiao, Kunlong Yin & Shuhao Liu

  2. School of Engineering and Technology, China University of Geosciences, Beijing, China

    Tianlu Yao

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  1. Ting Xiao
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  2. Kunlong Yin
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  3. Tianlu Yao
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  4. Shuhao Liu
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Correspondence to Ting Xiao.

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Xiao, T., Yin, K., Yao, T. et al. Spatial prediction of landslide susceptibility using GIS-based statistical and machine learning models in Wanzhou County, Three Gorges Reservoir, China. Acta Geochim 38, 654–669 (2019). https://doi.org/10.1007/s11631-019-00341-1

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  • DOI: https://doi.org/10.1007/s11631-019-00341-1

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