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
相关论文
共 50 条
  • [1] Disaster prediction model based on support vector machine for regression and improved differential evolution
    Yu, Xiaobing
    NATURAL HAZARDS, 2017, 85 (02) : 959 - 976
  • [2] Support Vector Machine Classification Prediction Model Based on Improved Chaotic Differential Evolution Algorithm
    Hou, Yaxin
    Ding, Xiangqian
    Hou, Ruichun
    2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD), 2017, : 123 - 129
  • [3] Prediction intervals for support vector machine regression
    Seok, K
    Hwang, C
    Cho, D
    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2002, 31 (10) : 1887 - 1898
  • [4] Prediction of network public opinion based on improved grey wolf optimized support vector machine regression
    Lin L.
    Chen F.
    Xie J.
    Li F.
    Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice, 2022, 42 (02): : 487 - 498
  • [5] AN IMPROVED SUPPORT VECTOR MACHINE MODEL BASED ON WAVECLUSTER
    Deng, Weiguo
    Wang, Li
    Qi, Jing
    Yu, Shan
    Xiang, Tiyan
    ICIM2012: PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON INDUSTRIAL MANAGEMENT, 2012, : 514 - 518
  • [6] Seasonal prediction of PM2.5 based on support vector machine model and multiple regression model
    Yang, Shuran
    INTERNATIONAL CONFERENCE ON ALGORITHMS, HIGH PERFORMANCE COMPUTING, AND ARTIFICIAL INTELLIGENCE (AHPCAI 2021), 2021, 12156
  • [7] Research on a Hybrid Prediction Model for Purchase Behavior Based on Logistic Regression and Support Vector Machine
    Hu, Xin
    Yang, Yanfei
    Zhu, Siru
    Chen, Lanhua
    2020 3RD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND BIG DATA (ICAIBD 2020), 2020, : 200 - 204
  • [8] A hybrid model based on support vector regression and differential evolution for remaining useful lifetime prediction of lithium-ion batteries
    Wang, Fu-Kwun
    Mamo, Tadele
    JOURNAL OF POWER SOURCES, 2018, 401 : 49 - 54
  • [9] Online prediction model based on support vector machine
    Wang, Wenjian
    Men, Changqian
    Lu, Weizhen
    NEUROCOMPUTING, 2008, 71 (4-6) : 550 - 558
  • [10] The chaos differential evolution optimization algorithm and its application to support vector regression machine
    Liang W.
    Zhang L.
    Wang M.
    Journal of Software, 2011, 6 (07) : 1297 - 1304
12345