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Machine Learning for Prediction and Risk Assessment of Landslides
- 1 Department of Electronic Information and Communication, Huazhong University of Science and Technology, Wuhan, 430000, China
- 2 The High School Affiliated to Renmin University of China, Beijing, 100080, China
- 3 Department of Electrical and Computer Engineering, The Ohio State University, Ohio, Columbus, 43210, The United States
* Author to whom correspondence should be addressed.
Abstract
Landslides pose a serious threat to human life and can cause substantial economic losses. It also consumes a lot of time and energy to deal with landslides. In order to address the occurrence of landslides, it is important to predict the probability of land- slides and assess the risk level. Since the occurrence of landslides is based on many factors, it is impossible for people to make an accurate judgment. Therefore, it is the best choice to train machines to help people to make predictions and risk assessment. In this paper, we discuss how to predict the occurrence of land- slides through random forests and assess the risk level according to the water content of the soil. The results show that the accuracy of prediction by Random Forest is very high. We will also predict future changes in soil moisture content to update the risk level assessment for real-time monitoring.
Keywords
Machine learning, Random forest, Landslides, Risk assessment, Wireless communication
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Cite this article
Zhou,X.;Wang,J.;Tian,L. (2025). Machine Learning for Prediction and Risk Assessment of Landslides. Applied and Computational Engineering,131,52-59.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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