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Forecasting flood disasters using an accelerated genetic algorithm: Examples of two case studies for China

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  • Ju-Liang Jin
  • Jian Cheng
  • Yi-Ming Wei

Abstract

This article discusses a rescaled range analysis model, titled AGA-R/S, that is based on an accelerated genetic algorithm. The parameter a, Hurst index of rescaled range analysis, and the recurrent time of disaster in the next time-period, were directly computed using an accelerated genetic algorithm developed by the authors. As case studies, using the AGA-R/S model, a forecast was made of the tendency for change in a time series of annual precipitation for the city of Jinhua, China. The model also forecast flooding-disaster in the city of Wuzhou, China. Results indicate that it is a relatively efficient technique to forecast the change-tendency of flood and disaster time series using the AGA-R/S model. When time series is utilized, forecasted error of the AGA-R/S model is less than with a linear least square method. The Hurst indexes of the two cities are from 0.23 to 0.24, which indicates that these time series are fractal and relatively long-term. Their fractional Brownian motion shows anti-persistence. AGA-R/S has application in forecasting the change-tendency of other natural disaster for specific time series. Copyright Springer Science+Business Media B.V. 2008

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  • Ju-Liang Jin & Jian Cheng & Yi-Ming Wei, 2008. "Forecasting flood disasters using an accelerated genetic algorithm: Examples of two case studies for China," 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. 44(1), pages 85-92, January.
    • Handle: RePEc:spr:nathaz:v:44:y:2008:i:1:p:85-92
    DOI: 10.1007/s11069-007-9143-0
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    1. Jim Hall & Paul Sayers & Richard Dawson, 2005. "National-scale Assessment of Current and Future Flood Risk in England and Wales," 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. 36(1), pages 147-164, September.
    2. Fumihiko Imamura & Dang Van To, 1997. "Flood and Typhoon Disasters in Viet Nam in the Half Century Since 1950," 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. 15(1), pages 71-87, January.
    3. Temi E. Ologunorisa & Ademola Adeyemo, 2005. "Public Perception of Flood Hazard in the Niger Delta, Nigeria," Environment Systems and Decisions, Springer, vol. 25(1), pages 39-45, March.
    4. Cheung, Yin-Wong & Lai, Kon S., 1995. "A search for long memory in international stock market returns," Journal of International Money and Finance, Elsevier, vol. 14(4), pages 597-615, August.
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    Cited by:

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    2. Giuseppe Orlando & Michele Bufalo, 2022. "A generalized two‐factor square‐root framework for modeling occurrences of natural catastrophes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1608-1622, December.
    3. Altay, Nezih & Narayanan, Arunachalam, 2022. "Forecasting in humanitarian operations: Literature review and research needs," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1234-1244.

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