Disaster prediction model based on support vector machine for regression and improved differential evolution

被引：0
|
作者：
Xiaobing Yu
机构：
[1] Nanjing University of Information Science and Technology,Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters
[2] Nanjing University of Information Science and Technology,Research Center for Prospering Jiangsu Province with Talents
[3] Nanjing University of Information Science and Technology,China Institute for Manufacture Developing
[4] Nanjing University of Information Science and Technology,School of Economics and Management
来源：
Natural Hazards | 2017年 / 85卷
关键词：
Support vector machine; Disaster prediction; Differential evolution; Hybrid model;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The kernel parameters setting of SVM influences prediction precision. The hybrid model based on SVM for regression and improved differential evolution is proposed to enhance the prediction precision. The improved differential evolution is used to optimize the kernel parameters. The improved differential evolution algorithm employs two trial vector generation strategies and two control parameter settings. The first-generation strategy is with best solution, and the second strategy is without best solution. Three categories of disasters time series including flood, drought and storm from Ministry of agriculture of China are used to verify the validity of the proposed model. Compared with the grid SVM and other models, the proposed hybrid model improves the prediction precision of SVM.
引用
收藏
页码：959 / 976
页数：17
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