Analysis of Geological Hazard Susceptibility of Landslides in Muli County Based on Random Forest Algorithm

Xiaoyi Wu, Yuanbao Song, Wei Chen, Guichuan Kang (), Rui Qu, Zhifei Wang, Jiaxian Wang, Pengyi Lv and Han Chen
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Xiaoyi Wu: Evaluation and Utilization of Strategic Rare Metals and Rare Earth Resource Key Laboratory of Sichuan Province & Sichuan Geological Survey, Chengdu 610081, China
Yuanbao Song: Evaluation and Utilization of Strategic Rare Metals and Rare Earth Resource Key Laboratory of Sichuan Province & Sichuan Geological Survey, Chengdu 610081, China
Wei Chen: Liangshan Prefecture Urban and Rural Land Comprehensive Consolidation and Reserve Center, Liangshan 615050, China
Guichuan Kang: College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Rui Qu: College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Zhifei Wang: College of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu 610059, China
Jiaxian Wang: Research Institute of Exploration and Development, PetroChina Southwest Oil & Gas Field Company, Chengdu 610051, China
Pengyi Lv: Research Institute of Exploration and Development, PetroChina Southwest Oil & Gas Field Company, Chengdu 610051, China
Han Chen: Sichuan Earthquake Agency, Chengdu 610041, China

Sustainability, 2023, vol. 15, issue 5, 1-17

Abstract: Landslides seriously threaten human life and property. The rapid and accurate prediction of landslide geological hazard susceptibility is the key to disaster prevention and mitigation. Traditional landslide susceptibility evaluation methods have disadvantages in terms of factor classification and subjective weight determination. Based on this, this paper uses a random forest model built using Python language to predict the landslide susceptibility of Muli County in western Sichuan and outputs the factor weight and model accuracy. The results show that (1) the three most important factors are elevation, distance from the road, and average annual rainfall, and the sum of their weights is 67.54%; (2) the model’s performance is good, with ACC = 99.43%, precision = 99.3%, recall = 99.48%, and F1 = 99.39%; (3) the landslide development and susceptibility zoning factors are basically the same. Therefore, this model can effectively and accurately evaluate regional landslide susceptibility. However, there are some limitations: (1) the landslide information statistical table is incomplete; (2) there are demanding requirements in terms of training concentration relating to the definition of landslide and non-landslide point sets, and the landslide range should be accurately delineated according to field surveys.

Keywords: random forest; landslide; susceptibility analysis; Muli County (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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