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

@article{citeulike:2552975, abstract = {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.}, author = {Jin, Ju-Liang and Cheng, Jian and Wei, Yi-Ming}, citeulike-article-id = {2552975}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/s11069-007-9143-0}, day = {22}, doi = {10.1007/s11069-007-9143-0}, journal = {Natural Hazards}, keywords = {data-reduction, genetic-algorithm, hydroinformatics, hydroinformatics-calibration, optimization}, month = {January}, number = {1}, pages = {85--92}, posted-at = {2008-03-18 21:12:14}, priority = {2}, title = {Forecasting flood disasters using an accelerated genetic algorithm: Examples of two case studies for China}, url = {http://dx.doi.org/10.1007/s11069-007-9143-0}, volume = {44}, year = {2008} }

TY - JOUR ID - citeulike:2552975 L3 - citeulike-article-id:2552975 N2 - 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. IS - 1 JF - Natural Hazards EP - 92 TI - Forecasting flood disasters using an accelerated genetic algorithm: Examples of two case studies for China VL - 44 SP - 85 KW - data-reduction KW - genetic-algorithm KW - hydroinformatics KW - hydroinformatics-calibration KW - optimization AU - Jin, Ju-Liang AU - Cheng, Jian AU - Wei, Yi-Ming PY - 2008/01/22/ UR - http://dx.doi.org/10.1007/s11069-007-9143-0 ER -