Susceptibility evaluation of valley debris flow based on dual-channel network with fusion attention mechanism

Susceptibility evaluation of valley debris flow based on dual-channel network with fusion attention mechanism

In addressing the issue of debris flow disaster assessment, this paper proposes a novel lightweight convolutional neural network model, the Dual-Channel Fusion Attention Mechanism Network (DCFAMNet), designed to rapidly identifying the susceptibility of gully-type debris flows. The main contribution...

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Bibliographic Details
Main Authors: Yumeng LUO, Baoyun WANG, Ruohao YUAN, Xu WANG, Cunxi LIU, Kuayue CHEN
Format: Article
Language:zho
Published: Editorial Office of The Chinese Journal of Geological Hazard and Control 2025-02-01
Series:Zhongguo dizhi zaihai yu fangzhi xuebao
Subjects:
convolutional neural network
debris flow
digital elevation map
remote sensing image
deep learning
susceptibility assessment
Online Access:https://www.zgdzzhyfzxb.com/en/article/doi/10.16031/j.cnki.issn.1003-8035.202305003
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https://www.zgdzzhyfzxb.com/en/article/doi/10.16031/j.cnki.issn.1003-8035.202305003

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