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.
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Acknowledgments
The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (NSFC) under the grants Nos.70425001 and 50579009, the Excellence Youth Teacher Sustentation Fund Program of Ministry of Education of China (Department of Education and Person [2002]350). We also would like to thank Professor T.S. Murty and the anonymous referees for their helpful suggestions and corrections on the earlier draft of our paper according to which we improved the content.
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Jin, JL., Cheng, J. & Wei, YM. Forecasting flood disasters using an accelerated genetic algorithm: Examples of two case studies for China. Nat Hazards 44, 85–92 (2008). https://doi.org/10.1007/s11069-007-9143-0
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DOI: https://doi.org/10.1007/s11069-007-9143-0