Typhoon disaster risk assessment based on attentional convolution module

Yongguo Shi; Jiayu Sun; Huihui Li; Shuhuai Mao; Yindong Zhang; Jiechao Fu

doi:10.1117/12.3035486

10 July 2024 Typhoon disaster risk assessment based on attentional convolution module

Yongguo Shi, Jiayu Sun, Huihui Li, Shuhuai Mao, Yindong Zhang, Jiechao Fu

Proceedings Volume 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024); 132230P (2024) https://doi.org/10.1117/12.3035486
Event: 2024 5th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2024), 2024, Wuhan, China

Abstract

Typhoon disasters have resulted in severe loss of life and property in Zhejiang Province. In order to further assess the risk level of typhoon disaster losses, this study takes Typhoon Lekima as the research object, considering risk indicators such as the risk factors of catastrophic factors, conceived disaster environment, and acceptor, and constructs a high-resolution typhoon disaster dataset based on kilometer grid units. Secondly, based on the disaster damage data of Typhoon Lekima, risk levels are delineated as output variables. Finally, a typhoon disaster risk assessment model is established using a convolutional neural network combined with the SENet attention mechanism. The results show that the model's validation accuracy exceeds 80%, and the introduction of the attention mechanism can also increase the model's evaluation accuracy by 1.46%. This achieves kilometer-scale disaster risk assessment of typhoon disasters, providing model data support for disaster prevention and mitigation efforts.

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

Citation Download Citation

Yongguo Shi, Jiayu Sun, Huihui Li, Shuhuai Mao, Yindong Zhang, and Jiechao Fu "Typhoon disaster risk assessment based on attentional convolution module", Proc. SPIE 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024), 132230P (10 July 2024); https://doi.org/10.1117/12.3035486

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