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

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  • Xiaobing Yu

    (Nanjing University of Information Science and Technology
    Nanjing University of Information Science and Technology
    Nanjing University of Information Science and Technology
    Nanjing University of Information Science and Technology)

Abstract

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.

Suggested Citation

  • Xiaobing Yu, 2017. "Disaster prediction model based on support vector machine for regression and improved differential evolution," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(2), pages 959-976, January.
    • Handle: RePEc:spr:nathaz:v:85:y:2017:i:2:d:10.1007_s11069-016-2613-5
    DOI: 10.1007/s11069-016-2613-5
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    References listed on IDEAS

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    Cited by:

    1. Dingli Liu & Zhisheng Xu & Chuangang Fan, 2019. "Predictive analysis of fire frequency based on daily temperatures," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 97(3), pages 1175-1189, July.
    2. Sheng, Jichuan & Qiu, Hong, 2018. "Governmentality within REDD+: Optimizing incentives and efforts to reduce emissions from deforestation and degradation," Land Use Policy, Elsevier, vol. 76(C), pages 611-622.

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