Collapse Susceptibility Assessment in Taihe Town Based on Convolutional Neural Network and Information Value Method

The cause mechanism of collapse disasters is complex and there are many influencing factors. Convolutional Neural Network (CNN) has a strong feature extraction ability, which can better simulate the formation of collapse disasters and accurately predict them. Taihe town’s collapse threatens roads, b...

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Bibliographic Details
Published in:Water (Basel) 2024-03, Vol.16 (5), p.709
Main Authors: Li, Houlu, Hu, Bill X., Lin, Bo, Zhu, Sihong, Meng, Fanqi, Li, Yufei
Format: Article
Language:English
Subjects:
Datasets
Deep learning
Disasters
Falls
Geographic information systems
Geology
Landslides & mudslides
Methods
Neural networks
Remote sensing
River networks
Roads & highways
Shear strength
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Summary:The cause mechanism of collapse disasters is complex and there are many influencing factors. Convolutional Neural Network (CNN) has a strong feature extraction ability, which can better simulate the formation of collapse disasters and accurately predict them. Taihe town’s collapse threatens roads, buildings, and people. In this paper, road distance, water distance, normalized vegetation index, platform curvature, profile curvature, slope, slope direction, and geological data are used as input variables. This paper generates collapse susceptibility zoning maps based on the information value method (IV) and CNN, respectively. The results show that the accuracy of the susceptibility assessment of the IV method and the CNN method is 85.1% and 87.4%, and the accuracy of the susceptibility assessment based on the CNN method is higher. The research results can provide some reference for the formulation of disaster prevention and control strategies.
ISSN:2073-4441
2073-4441
DOI:10.3390/w16050709