Therelationbetweenthewaterdischarge(Q)andsuspendedsedimentconcentration(SSC)oftheRiverRamgangaatBareilly,UttarPradesh,intheHimalayas,hasbeenmodeledusingArtificialNeuralNetworks(ANNs).Thecurrentstudyvalidatesthepracticalcapabilityandusefulnessofthistoolforsimulatingcomplexnonlinear,realworld,riversystemprocessesintheHimalayanscenario.ThemodelingapproachisbasedonthetimeseriesdatacollectedfromJanuarytoDecember(2008-2010)forQandSSC.ThreeANNs(T1-T3)withdifferentnetworkconfigurationshavebeendevelopedandtrainedusingtheLevenbergMarquardtBackPropagationAlgorithmintheMatlabroutines.Networkswereoptimizedusingtheenumerationtechnique,and,finally,thebestnetworkisusedtopredicttheSSCvaluesfortheyear2011.ThevaluesthusobtainedthroughtheANNmodelarecomparedwiththeobservedvaluesofSSC.Thecoefficientofdetermination(R2),fortheoptimalnetworkwasfoundtobe0.99.ThestudynotonlyprovidesinsightintoANNmodelingintheHimalayanriverscenario,butitalsofocusesontheimportanceofunderstandingariverbasinandthefactorsthataffecttheSSC,beforeattemptingtomodelit.Despitethetemporalvariationsinthestudyarea,itispossibletomodelandsuccessfullypredicttheSSCvalueswithverysimplisticANNmodels.
