Risk Mapping of Geological Hazards in Plateau Mountainous Areas Based on Multisource Remote Sensing Data Extraction and Machine Learning (Fuyuan, China)

Shaohan Zhang, Shucheng Tan (), Yongqi Sun, Duanyu Ding and Wei Yang
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Shaohan Zhang: Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China
Shucheng Tan: Yunnan International Joint Laboratory of Critical Mineral Resource, Kunming 650500, China
Yongqi Sun: Yunnan International Joint Laboratory of Critical Mineral Resource, Kunming 650500, China
Duanyu Ding: Faculty of Architecture and City Planning, Kunming University of Science and Technology, Kunming 650500, China
Wei Yang: Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China

Land, 2024, vol. 13, issue 9, 1-25

Abstract: Selecting the most effective prediction model and correctly identifying the main disaster-driving factors in a specific region are the keys to addressing the challenges of geological hazards. Fuyuan County is a typical plateau mountainous town, and slope geological hazards occur frequently. Therefore, it is highly important to study the spatial distribution characteristics of hazards in this area, explore machine learning models that can be highly matched with the geological environment of the study area, and improve the accuracy and reliability of the slope geological hazard risk zoning map (SGHRZM). This paper proposes a hazard mapping research method based on multisource remote sensing data extraction and machine learning. In this study, we visualize the risk level of geological hazards in the study area according to 10 pathogenic factors. Moreover, the accuracy of the disaster point list was verified on the spot. The results show that the coupling model can maximize the respective advantages of the models used and has highest mapping accuracy, and the area under the curve (AUC) is 0.923. The random forest (RF) model was the leader in terms of which single model performed best, with an AUC of 0.909. The grid search algorithm (GSA) is an efficient parameter optimization technique that can be used as a preferred method to improve the accuracy of a model. The list of disaster points extracted from remote sensing images is highly reliable. The high-precision coupling model and the single model have good adaptability in the study area. The research results can provide not only scientific references for local government departments to carry out disaster management work but also technical support for relevant research in surrounding mountainous towns.

Keywords: machine learning model; remote sensing datasets; disaster point extraction; geological hazard risk mapping (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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