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

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• 作者：Xiaobing Yu
• 关键词：Support vector machine ; Disaster prediction ; Differential evolution ; Hybrid model
• 刊名：Natural Hazards
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：85
• 期：2
• 页码：959-976
• 全文大小：
• 刊物类别：Earth and Environmental Science
• 刊物主题：Natural Hazards; Hydrogeology; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Civil Engineering; Environmental Management;
• 出版者：Springer Netherlands
• ISSN：1573-0840
• 卷排序：85

文摘

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.