School of Economics, Ocean University of China, Qingdao 266100, China.
College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China.
Int J Environ Res Public Health. 2018 Mar 27;15(4):604. doi: 10.3390/ijerph15040604.
Storm surge has become an important factor restricting the economic and social development of China's coastal regions. In order to improve the scientific judgment of future storm surge damage, a method of model groups is proposed to refine the evaluation of the loss due to storm surges. Due to the relative dispersion and poor regularity of the natural property data (login center air pressure, maximum wind speed, maximum storm water, super warning water level, etc.), storm surge disaster is divided based on eight kinds of storm surge disaster grade division methods combined with storm surge water, hypervigilance tide level, and disaster loss. The storm surge disaster loss measurement model groups consist of eight equations, and six major modules are constructed: storm surge disaster in agricultural loss, fishery loss, human resource loss, engineering facility loss, living facility loss, and direct economic loss. Finally, the support vector machine (SVM) model is used to evaluate the loss and the intra-sample prediction. It is indicated that the equations of the model groups can reflect in detail the relationship between the damage of storm surges and other related variables. Based on a comparison of the original value and the predicted value error, the model groups pass the test, providing scientific support and a decision basis for the early layout of disaster prevention and mitigation.
风暴潮已成为制约中国沿海地区经济社会发展的重要因素。为提高未来风暴潮灾害损失的科学判断水平，提出了一种利用模型组方法细化风暴潮损失评估的方法。由于自然属性数据（登陆中心气压、最大风速、最大风暴潮水位、超警戒潮位等）具有相对分散性和较差的规律性，因此结合风暴潮水位、超警戒潮位和灾害损失，将风暴潮灾害按照八种风暴潮灾害等级划分方法进行了划分。风暴潮灾害损失测算模型组由八个方程组成，构建了六个主要模块：农业损失、渔业损失、人力资源损失、工程设施损失、生活设施损失和直接经济损失。最后，利用支持向量机（SVM）模型对损失进行评估和样本内预测。结果表明，模型组的方程能够详细反映风暴潮灾害破坏与其他相关变量之间的关系。通过对原始值和预测值误差的比较，模型组通过了测试，为防灾减灾的早期布局提供了科学支撑和决策依据。
