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Economic loss assessment of typhoon-induced storm surge disasters in the South China Sea based on GSA-BP model  ( SCI-EXPANDED收录)   被引量:2

文献类型:期刊文献

英文题名:Economic loss assessment of typhoon-induced storm surge disasters in the South China Sea based on GSA-BP model

作者:Zhang, Yuxuan[1];Zhang, Tianyu[1,2,3];Shen, Wenqi[1];Ou, Zijing[1];Zhang, Junping[1]

机构:[1]Guangdong Ocean Univ, Coll Ocean & Meteorol, Zhanjiang, Guangdong, Peoples R China;[2]Guangdong Ocean Univ, Coll Ocean & Meteorol, Lab Coastal Ocean Variat & Disaster Predict, Zhanjiang, Guangdong, Peoples R China;[3]Guangdong Ocean Univ, Key Lab Climate Resources & Environm Continental S, Dept Educ Guangdong Prov, Zhanjiang, Guangdong, Peoples R China

年份:2023

卷号:11

外文期刊名:FRONTIERS IN EARTH SCIENCE

收录:SCI-EXPANDED(收录号:WOS:001138479300001)、、Scopus(收录号:2-s2.0-85181870298)、WOS

基金:The authors declare financial support was received for the research, authorship, and/or publication of this article. This research was jointly funded by National Key Research and Development Program of China (Grant number 2021YFC3101801, 2022YFC3103104); Innovative Team Plan for Department of Education of Guangdong Province (No. 2023KCXTD015);Guangdong Science and Technology Plan Project (Observation of Tropical marine environment in Yuexi); Independent research project of Southern Ocean Laboratory (Grant number SML2022SP301); National Natural Science Foundation of China (Grant number 41976200); Guangdong Ocean University Scientific Research Program (Grant number 060302032106).

语种:英文

外文关键词:maximum wind speed of typhoons; storm surge; direct economic losses; genetic simulated annealing algorithm; BP neural network

外文摘要:In the context of global climate warming and rising sea levels, the frequency of tropical cyclones in the South China Sea region has shown a significant upward trend in recent years. Consequently, the coastal areas of the South China Sea are increasingly vulnerable to storm surge disasters induced by typhoon, posing severe challenges to disaster prevention and mitigation in affected cities. Therefore, establishing a multi-indicator assessment system for typhoon storm surges is crucial to provide scientific references for effective defense measures against disasters in the region. This study examines 25 sets of typhoon storm surge data from the South China Sea spanning the years 1989-2020. A comprehensive assessment system was constructed to evaluate the damages caused by storm surges by incorporating the maximum wind speed of typhoons. To reduce redundancy among multiple indicators in the assessment system and enhance the stability and operational efficiency of the storm surge-induced disaster loss model, the entropy method and bootstrap toolbox were employed to process post-disaster data. Furthermore, the genetic simulated annealing algorithm was utilized to optimize a backpropagation neural network intelligent model (GSA-BP), enabling pre-assessment of the risks associated with storm surge disasters induced by typhoon and related economic losses. The results indicate that the GSA-BP model outperforms the genetic algorithm optimized BP model (GA-BP) and the simulated annealing algorithm-optimized BP model (SA-BP) in terms of predicting direct economic losses caused by storm surges. The GSA-BP model exhibits higher prediction accuracy, shorter computation time, and faster convergence speed. It offers a new approach to predicting storm surge losses in coastal cities along the South China Sea.

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