IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v85y2017i2d10.1007_s11069-016-2613-5.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-016-2613-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-016-2613-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wong, W.K. & Xia, Min & Chu, W.C., 2010. "Adaptive neural network model for time-series forecasting," European Journal of Operational Research, Elsevier, vol. 207(2), pages 807-816, December.
    2. Zaiwu Gong & Caiqin Chen & Xinming Ge, 2014. "Risk prediction of low temperature in Nanjing city based on grey weighted Markov model," 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. 71(2), pages 1159-1180, March.
    3. Zhang, Feng, 2007. "An application of vector GARCH model in semiconductor demand planning," European Journal of Operational Research, Elsevier, vol. 181(1), pages 288-297, August.
    4. Gaalman, Gerard & Disney, Stephen M., 2006. "State space investigation of the bullwhip problem with ARMA(1,1) demand processes," International Journal of Production Economics, Elsevier, vol. 104(2), pages 327-339, December.
    5. Murat, Yetis Sazi & Ceylan, Halim, 2006. "Use of artificial neural networks for transport energy demand modeling," Energy Policy, Elsevier, vol. 34(17), pages 3165-3172, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    2. Buratti, C. & Barbanera, M. & Palladino, D., 2014. "An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks," Applied Energy, Elsevier, vol. 120(C), pages 125-132.
    3. Erdem Doğan, 2020. "Analysis of the relationship between LSTM network traffic flow prediction performance and statistical characteristics of standard and nonstandard data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1213-1228, December.
    4. Silva, Mafalda C. & Horta, Isabel M. & Leal, Vítor & Oliveira, Vítor, 2017. "A spatially-explicit methodological framework based on neural networks to assess the effect of urban form on energy demand," Applied Energy, Elsevier, vol. 202(C), pages 386-398.
    5. Cang, Shuang & Yu, Hongnian, 2014. "A combination selection algorithm on forecasting," European Journal of Operational Research, Elsevier, vol. 234(1), pages 127-139.
    6. Jiang, Yu & Song, Zhe & Kusiak, Andrew, 2013. "Very short-term wind speed forecasting with Bayesian structural break model," Renewable Energy, Elsevier, vol. 50(C), pages 637-647.
    7. Manuel Llorca & José Baños & José Somoza & Pelayo Arbués, 2017. "A Stochastic Frontier Analysis Approach for Estimating Energy Demand and Efficiency in the Transport Sector of Latin America and the Caribbean," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    8. Tehreem Fatima & Enjun Xia & Muhammad Ahad, 2019. "Oil demand forecasting for China: a fresh evidence from structural time series analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(3), pages 1205-1224, June.
    9. Aydin, Gokhan, 2014. "Modeling of energy consumption based on economic and demographic factors: The case of Turkey with projections," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 382-389.
    10. Yi-Ting Chen & Edward W. Sun & Yi-Bing Lin, 2020. "Machine learning with parallel neural networks for analyzing and forecasting electricity demand," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 569-597, August.
    11. Llorca, Manuel & Baños, José & Somoza, José & Arbués, Pelayo, 2014. "A latent class approach for estimating energy demands and efficiency in transport: An application to Latin America and the Caribbean," Efficiency Series Papers 2014/04, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    12. Barrow, Devon K. & Crone, Sven F., 2016. "A comparison of AdaBoost algorithms for time series forecast combination," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1103-1119.
    13. SeyedAli Ghahari & Cesar Queiroz & Samuel Labi & Sue McNeil, 2021. "Cluster Forecasting of Corruption Using Nonlinear Autoregressive Models with Exogenous Variables (NARX)—An Artificial Neural Network Analysis," Sustainability, MDPI, vol. 13(20), pages 1-20, October.
    14. Jakov Topić & Branimir Škugor & Joško Deur, 2019. "Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range," Energies, MDPI, vol. 12(7), pages 1-20, April.
    15. Kankal, Murat & AkpInar, Adem & Kömürcü, Murat Ihsan & Özsahin, Talat Sükrü, 2011. "Modeling and forecasting of Turkey's energy consumption using socio-economic and demographic variables," Applied Energy, Elsevier, vol. 88(5), pages 1927-1939, May.
    16. Fang, Guochang & Tian, Lixin & Fu, Min & Sun, Mei, 2014. "Government control or low carbon lifestyle? – Analysis and application of a novel selective-constrained energy-saving and emission-reduction dynamic evolution system," Energy Policy, Elsevier, vol. 68(C), pages 498-507.
    17. Al-Ghandoor, Ahmed & Jaber, Jamal & Al-Hinti, Ismael & Abdallat, Yousef, 2013. "Statistical assessment and analyses of the determinants of transportation sector gasoline demand in Jordan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 50(C), pages 129-138.
    18. Ceylan, Huseyin & Ceylan, Halim & Haldenbilen, Soner & Baskan, Ozgur, 2008. "Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey," Energy Policy, Elsevier, vol. 36(7), pages 2527-2535, July.
    19. Kavaklioglu, Kadir, 2011. "Modeling and prediction of Turkey's electricity consumption using Support Vector Regression," Applied Energy, Elsevier, vol. 88(1), pages 368-375, January.
    20. Xi Gang Yuan & Nan Zhu, 2016. "Bullwhip Effect Analysis in Two-Level Supply Chain Distribution Network Using Different Demand Forecasting Technology," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 33(03), pages 1-23, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:85:y:2017:i:2:d:10.1007_s11069-016-2613-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.