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

Ju-Liang Jin, Jian Cheng and Yi-Ming Wei

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2008, vol. 44, issue 1, 85-92

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

Keywords: Flood disaster; Forecast; R/S analysis; Fractal; Hurst index; Genetic algorithm (search for similar items in EconPapers)
Date: 2008
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DOI: 10.1007/s11069-007-9143-0

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