基于SVM-BP神经网络的风暴潮灾害损失预评估

风暴潮灾害是影响我国最严重的海洋灾害，风暴潮灾害损失的预评估对防灾减灾有重要作用。本文选用2002~2014年的40组风暴潮历史灾情资料进行试验，首先建立风暴潮灾害损失评估指标体系并用灰色关联分析法对指标进行筛选，然后采用最优权重组合将支持向量机和BP神经网络进行组合预测分别对风暴潮直接经济损失和受灾人口数进行预测，并与单一预测方法进行对比，发现组合预测方法可以降低误差，提高损失预测的准确性，建立风暴潮灾害损失预评估模型，为决策者进行预警信息的发布提供有效依据。

Pre-assessment for the loss caused by storm surge based on the SVM-BP neural network

Storm surge disaster is one of the most serious marine disasters in China, and the pre-assessment of storm surge disaster has an important role in disaster prevention and mitigation.In this paper, 40 sets of storm surge disaster data from 2002 to 2014 were selected for experiment.This paper establishes the index system of storm surge disaster and simplify the indicators by gray correlation analysis method.The combination model with SVM and BP neural network forecasts the direct economic losses and the affected population number of storm surge respectively, and with single prediction methods were compared.It can be found that the combination forecasting model can reduce the error, provided effective basis for decision makers during disaster management.