Demirdag O.Yurdusev M.A.Solmaz B.2024-07-222024-07-222004http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/19917In this project, artificial neural networks (ANN) are used for the estimation of water quality parameters of River Gediz in the western part of Turkey. Gediz River basin contains the third largest city of Turkey. Moreover, it is the major agricultural area in the region as well as highly industrialised. There are so many pollution sources although clean water is highly demanded. Therefore, estimation of quality parameters from relatively easily measured river parameters is of great importance to maintain adequate water quality monitoring in the river. Selection of appropriate input parameters to estimate another one is key to use ANN. This is essential to obtain maximum success with minimum error. The input parameters selected must be those which affects most the output one. In this study, therefore, to estimate the amount of total dissolved solid (TDS), the river discharge, the water temperature and PH (acidity) are chosen as input parameters. A generic ANN software was run for different values of input parameters. This comprised training and test phases. For the most successful result of training for each month, the test phase was undertaken. The values passing the test phase successfully showed that ANN could successfully be used for estimation of water quality parameters that are relatively difficult to measure from those easily-measured. The outputs of such studies are essential for river water quality modelling studies. Copyright ASCE 2004.EnglishComputer software selection and evaluationDissolutionEstimationNeural networksPlanningRiversWater pollutionWater qualityWater resources explorationAgricultural areasArtificial Neural NetworkClean watersGediz RiverInput parameterParametersPollution sourcesQuality parametersRiver dischargeRiver water qualityTest phasisTotal dissolved solidsWater quality monitoringWater quality parametersWater temperaturesWater resourcesConference paper10.1061/40517(2000)220