TyphoonSurgeForecasting withArtificial Back-propagation Neural Networks

A typhoon-surge forecasting modelwasdeveloped withtheapplication oftheback-propagation neural network (BPN)inthepresent paper.Thisartificial neural network I modelforecasts thehourlytimeseries oftyphoonsurge _ variation basedon a setofinput dataincluding typhoon's characteristics, localmeteorological conditions andtyphoon surges ata considered tidal station. Forselecting a better eents forecasting model, fourmodels (Models A,B,C,andD)were %53N tested andcompared underthedifferent composition ofinput factors. A general evaluation indexthatisa composition of fourperformance indexes wasproposed toevaluate themodel's D nI4d overall performance. Testedresults showthatModelD iit@Wi O 3952M1XX M k composing 18input factors hasbestperformance amongthe POO4o i I~~~~* The,numericals,imuflation techninque- s1tatis-t icalq theeastcoast andfurther degrade after passing overtheTe nueria siuato tehiqe sttsia Central Mountain Range.According totheCentral Weather (empirical) analysis andartificial neural network havebeen Bureau~~ ~