科研之友
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摘要
Hierarchical comprehensive evaluation method is widely used in flood disaster loss assessment and risk prediction of strength. How to improve its performance and tempo is still a continuous research problem. Support vector machine (SVM) is proved to be one of most effective method to solve the classification problem of small samples. However, the traditional SVM does not reflect difference of different indexes and leads to error. So we propose the combined weighted SVM (CWSVM) to evaluate flood disaster grade. The model modifies the kernel function of SVM and solves the problem that samples' Euclidian distances are necessary to really embody the feature difference. Besides, the model modifies the distance by both the objective value difference and the subjective human conventions. By comparative analyzing the assessment results of flood disaster data in China from 1950 to 2009, the CWSVM obtained higher classification precision. The research offers a new efficient way to solve multi-index comprehensively evaluation problem.
- 出版日期2013-4
- 单位湖北经济学院
