Landslide susceptibility of Western Tibet Plateau based on the random forest model

Yongpeng Yang; Hao Chen; Ya Guo; Tang Hao; Youyun Yang; Yu Bian

doi:10.1117/12.3020866

19 January 2024 Landslide susceptibility of Western Tibet Plateau based on the random forest model

Yongpeng Yang, Hao Chen, Ya Guo, Tang Hao, Youyun Yang, Yu Bian

Proceedings Volume 12980, Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023); 129800F (2024) https://doi.org/10.1117/12.3020866
Event: Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023), 2023, Lianyungang, China

Abstract

Landslide is a very important geological disaster, which causes a large number of people's lives and property losses every year. Landslide susceptibility map is one of the effective methods to prevent and control disaster losses. However, the application of machine learning in susceptibility map under the high altitude localities is relatively lacking. Here we show that the RF model which is one of the machine learning methods can acquire a reliable susceptibility map. On this paper, we chose aspect, slope, lithology, fault, elevation, river and road as the factors to find the susceptibility map. TheIVM was applied as the first model to acquire the information of the factors. The coefficients of the factors were got by the RF model finally, the result was divided into five grades from very high to by very low by natural breakpoint method. Through the ROC curves, the credibility level is 86.3%. Therefore, this result can provide data support for local disaster prevention and mitigation work. At the same time, this study at the area revealed that the machine learning can be applied on the susceptibility maps and it were credible.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yongpeng Yang, Hao Chen, Ya Guo, Tang Hao, Youyun Yang, and Yu Bian "Landslide susceptibility of Western Tibet Plateau based on the random forest model", Proc. SPIE 12980, Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023), 129800F (19 January 2024); https://doi.org/10.1117/12.3020866

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