Application of new Artificial Intelligence Methods in Groundwater Quality Assessment (Case Study: Salmas Plain)

Given all the advancements in water resources management engineering, the problem of groundwater quality assessment is the main problem encountered in most plains of Iran. Therefore, managing and monitoring the quality of water resources is very importance. In this study, we tried to predict and estimate the groundwater quality in the Salmas plain using RBF and GFF models. To achieve this aim, groundwater quality data of Salmas plain during 10 years (2001-2011) were used and results were analyzed according to Wilcox, Scholler and Piper standards. 70% of data were used to train the network and 10% of data were used to validate the two models. Therefore, the remaining 20% ​​of available data was used for network testing. The application of appropriate and applicable statistical parameters showed that RBF model with Levenberg-Marquardt training and 4 hidden layers, has high ability to estimate and predict groundwater quality. Also R2= 0.88 and RMSE= 29.71% in this model. Also the results of using different diagrams show that samples have low hardness and corrosion. Most of the data is in the C3S1 class. According to the results, all the water resources of the study area are acceptable for agriculture, drinking and industry, respectively.