Research on geological hazard risk assessment based on machine learning

Heng Liu; Zhi Zeng; Tao Zuo; Kang Jia; RuoYu Wang

doi:10.1117/12.3035710

10 July 2024 Research on geological hazard risk assessment based on machine learning

Heng Liu, Zhi Zeng, Tao Zuo, Kang Jia, RuoYu Wang

Proceedings Volume 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024); 1322304 (2024) https://doi.org/10.1117/12.3035710
Event: 2024 5th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2024), 2024, Wuhan, China

Abstract

To address the issues of inaccurate coefficient estimates for geological disaster risk assessment factors and unreasonable risk zoning, this paper proposes a grid-based machine learning model for geological disaster risk assessment, taking a multi-factor and multi-characteristic approach to geological disasters. Firstly, geological disaster data from Wenzhou city is collected and organized. Using GIS spatial analysis and attribute calculation techniques, seven factors including landform type, terrain slope, road construction, geological structure, soil type, river distribution, and vegetation coverage are used as evaluation factors for geological disasters. A neural network model is introduced to optimize the calculation of factor weights. Finally, a geological disaster risk factor evaluation map is derived using weighted analysis. The research results show that the use of a machine learning model to automatically calculate evaluation factors and weight coefficients, broken down by grid units, enhances the rationality of geological disaster risk assessment and can provide a scientific reference for regional geological disaster prevention and mitigation management.

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

Citation Download Citation

Heng Liu, Zhi Zeng, Tao Zuo, Kang Jia, and RuoYu Wang "Research on geological hazard risk assessment based on machine learning", Proc. SPIE 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024), 1322304 (10 July 2024); https://doi.org/10.1117/12.3035710

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