台风风暴潮损失评估方式的优化分析

全球气候变化背景下，海平面上升和温室气体的大量排放加剧了台风风暴潮损失，构建准确的损失评估模型对海洋防灾减灾工作有重大现实意义。本文选用1995-2020年50组广东省台风风暴潮进行试验，基于气候变化和风险评估理论建立台风风暴潮损失评估指标体系，使用主成分分析筛选输入因子，进行RBF神经网络和支持向量回归模型的直接经济损失和海水养殖受灾面积评估，通过比较有无气候变化指标评估模型的结果，验证了气候变化是影响灾害损失的重要因素。采用熵权法组合RBF神经网络和支持向量回归模型进行直接经济损失评估，对比单一模型发现组合模型有更好的预测精度，为防灾减灾事业提供了有效的损失评估方式。

Optimization analysis of typhoon storm surge loss assessment method

Under the background of global climate change, the rising sea level and the massive emission of greenhouse gases aggravate the loss of typhoon storm surge. It is of great practical significance to build an accurate loss assessment model for Marine disaster prevention and mitigation.In this paper, 50 groups of typhoon storm surges in Guangdong Province from 1995 to 2020 were selected for the experiment. Based on the theory of climate change and risk assessment, the index system of typhoon storm surges loss assessment was established. Principal component analysis was used to screen input factors, and RBF neural network and support vector machine for regression model were used to evaluate the direct economic losses and the affected area of mariculture. By comparing the results of evaluation models with and without climate change indicators, it is verified that climate change is an important factor affecting disaster losses.Entropy weight method combined with RBF neural network and support vector machine for regression model is used to evaluate direct economic losses. Compared with single model, the combined model has better prediction accuracy, which provides a reasonable and effective loss assessment method for disaster prevention and reduction.