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The forecasting of inundation levels during typhoonsrequires that multiple objectives be taken into account, including theforecasting capacity with regard to variations in water level throughout theentire weather event, the accuracy that can be attained in forecasting peakwater levels, and the time at which peak water levels are likely to occur.This paper proposed a means of forecasting inundation levels in real timeusing monitoring data from a water-level gauging network. ARMAX was used toconstruct water-level forecast models for each gauging station using inputvariables including cumulative rainfall and water-level data from othergauging stations in the network. Analysis of the correlation betweencumulative rainfall and water-level data makes it possible to obtain theappropriate accumulation duration of rainfall and the time lags associatedwith each gauging station. Analyses on cross-site water levels as well as oncumulative rainfall enable the identification of  associate sites pertaining toeach gauging station that share high correlations with regard to water leveland low mutual information with regard to cumulative rainfall. Water-leveldata from the identified associate sites are used as a second input variablefor the water-level forecast model of the target site. Three indices wereconsidered in the selection of an optimal model: the coefficient ofefficiency (CE), error in the stage of peak water level (ESP), and relativetime shift (RTS). A multi-objective genetic algorithm was employed to derivean optimal Pareto set of models capable of performing well in the threeobjectives. A case study was conducted on the Xinnan area of Yilan County,Taiwan, in which optimal water-level forecast models were established foreach of the four water-level gauging stations in the area. Test resultsdemonstrate that the model best able to satisfy ESP exhibited significanttime shift, whereas the models best able to satisfy CE and RTS provideaccurate forecasts of inundations when variations in water level are lessextreme

natural hazards and earth system sciences

Multi-objective optimization of typhoon inundation forecast models with cross-site structures for a water-level gauging network by integrating ARMAX with a genetic algorithm