Assessment of debris flow susceptibility based on dual-channel feature fusion CNN model

Xu Wang; Baoyun Wang

doi:10.1117/12.3029033

22 May 2024 Assessment of debris flow susceptibility based on dual-channel feature fusion CNN model

Xu Wang, Baoyun Wang

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 1317611 (2024) https://doi.org/10.1117/12.3029033
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

Debris flow is a natural geological disaster that frequently occurs in mountainous areas, posing a serious threat to the lives and property of local residents. However, conducting large-scale on-site investigations of debris flows is challenging due to the complex terrain of these areas. To address this issue, a CNN model based on dual-channel feature fusion is proposed to assess the susceptibility of debris flows. First, a dual-channel architecture is constructed, where a 2D CNN extracts the spatial features of the DEM image, and a 3D CNN extracts the spatial-spectral features of the multispectral image. Second, the residual structure is improved for feature extraction, and a CBAM block is added to enhance the network's ability to extract key features of valley images. Then, a fusion module is designed to fully integrate the features of the two channels. Finally, the susceptibility index is calculated based on the similarity score, and the susceptibility assessment results are divided into five levels and verified. The proposed model achieves an accuracy of 78.62% in the valley classification task and shows promising results in assessing the susceptibility of debris flows. Specifically, the proportion of high and moderately susceptible areas is 75.52%, the debris flow ratio is 96.38%, and the frequency ratio precision is 93.45%. These results demonstrate the feasibility of the proposed susceptibility assessment method and highlight its potential as a reference for debris flow prevention and disaster reduction efforts.

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

Citation Download Citation

Xu Wang and Baoyun Wang "Assessment of debris flow susceptibility based on dual-channel feature fusion CNN model", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 1317611 (22 May 2024); https://doi.org/10.1117/12.3029033

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KEYWORDS

Feature extraction

Data modeling

Image fusion

Performance modeling

Feature fusion

Convolution

Statistical modeling

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