Yapay sinir ağları yardımı ile yeraltı suyunda sodyum absorbsiyon oranı (SAR) tahmini: Ergene havzası doğu akiferi örneği

Groundwater is used for drinking and irrigation purposes in many parts of the world. Irrigation practices result in the deterioration of the quality of the groundwater over the time and this adversely affects the human health and plant growth. Monitoring of the vulnerable aquifers with cost-effective methods is important. In this study an artificial neural network model is proposed for the prediction of sodium absorption ratio (SAR) in the unconfined aquifer, located in the east of Ergene basin. The samples taken from 18 observation wells were analysed monthly for electrical conductivity, total dissolved solids, temperature, total hardness, chloride and pH. Levenberg–Marquardt (trainlm) was selected for backpropagation algorithm and 35 neurons were used in the model architecture. The model follows up the experimental data very closely (R= 0,855). Application of the proposed model would make possible to monitor the aquifers in a more cost-effective and easier way.