| 摘要: |
| 近年来全球气候变化加剧，台风风暴潮灾害的频率、强度和损失逐渐加大，台风风暴潮灾害损失的预评估对海洋防灾减灾工作有重大现实意义。作者选用广东省1995年-2020年间的50组台风风暴潮数据进行研究，量化气候变化数据，建立台风风暴潮损失评估体系并通过主成分分析进行降维。采用麻雀搜索算法优化极限学习机建立预评估模型，分别对台风风暴潮损失等级、受灾人口和直接经济损失进行预测，结果表明，优化后的模型正确率更高，且具有更好的预测精确性和适用性，为防灾减灾事业提供了有效的损失评估方式。 |
| 关键词: 台风风暴潮 损失预评估 麻雀搜索算法(SSA) 极限学习机 |
| DOI：10.11759/hykx20210607003 |
| 分类号:X43、P732 |
| 基金项目:国家自然科学基金项目（41072176，41371496）；国家科技支撑计划项目（2013BAK05B04） |
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| Pre-assessment of typhoon storm surge disaster loss based on the SSA-ELM model |
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HAO Jing, LIU Qiang
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College of Engineering, Ocean University of China, Qingdao 266100, China
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| Abstract: |
| In recent years, global climate change has intensified, and the frequency, intensity, and loss of typhoon storm surge disasters have gradually increased. Pre-assessing typhoon storm surge disaster losses has a considerable practical significance for marine disaster prevention and mitigation. This paper selects 50 sets of typhoon storm surge data in Guangdong Province from 1995 to 2020, quantifies climate change data, establishes a typhoon storm surge loss assessment system, and reduces the dimensionality through principal component analysis. The sparrow search algorithm is used to optimize the extreme learning machine to establish a pre-evaluation model, which predicts the typhoon storm surge loss level, the affected population, and the direct economic loss. The results show that the optimized model has a higher accuracy rate and better prediction accuracy and applicability. Further, this paper provides an effective loss assessment method for disaster prevention and mitigation. |
| Key words: typhoon storm surge loss pre-assessment sparrow search algorithm(SSA) extreme learning machine |
