Morvarid Derakhshan Andisheh Journal of Quality Engineering and Management 2322-1305 13 2 2023 08 23 Evaluating the capability of artificial intelligence in predicting the amount of electrical conductivity and nitrate in underground water resources (a case study of artificial neural methods ANN and ANFIS) Evaluating the capability of artificial intelligence in predicting the amount of electrical conductivity and nitrate in underground water resources (a case study of artificial neural methods ANN and ANFIS) 207 230 192560 10.48313/jqem.2023.192560 FA Navideh Najafpour Expert of Environment and Water quality office in Esfahan regional water company 0000-0002-2783-4588 Niaz Vahdatpour Head of water quality office in Esfahan regional water company 0000-0002-2783-4588 Elham Aghababaei Expert of environment and water quality office at Esfahan regional water company 0000-0002-2783-4588 Journal Article 2024 02 12 In this study, the usual kriging method as a linear statistical estimator and two intelligent methods of artificial neural network ANN and adaptive neural fuzzy inference system ANFIS were evaluated in predicting the amount of electric conductivity and nitrate in groundwater. In order to conduct studies, nitrate concentration in 40 wells in Lanjanat plain of Isfahan was measured by spectrophotometer and electrical conductivity. The input data of the artificial neural model, including the length and width of the geographies, the nitrate concentration, and the electrical conductivity value were determined as the output of the model. In order to investigate the performance and efficiency of artificial intelligence models in predicting qualitative information, qualitative information of 50% of the wells was used for calibration and 50% of the wells were used for validating the models. Finally, the output of the models was compared with the value measured in the observation wells based on the mutual error evaluation criteria. The results showed that the ANFIS model performed better than the other two interpolation models in predicting the value of electrical conductivity and nitrate, respectively, with the root mean square error (RMSE) and (mg/l) of 5.362, with the mean bias error (MBE) 2.365 with a correlation coefficient (R) of 0.767. Also, the ANN model had far better results than the usual kriging method. Based on this, ANFIS model is proposed for spatial prediction of electrical conductivity and nitrate in the study area. In this study, the usual kriging method as a linear statistical estimator and two intelligent methods of artificial neural network ANN and adaptive neural fuzzy inference system ANFIS were evaluated in predicting the amount of electric conductivity and nitrate in groundwater. In order to conduct studies, nitrate concentration in 40 wells in Lanjanat plain of Isfahan was measured by spectrophotometer and electrical conductivity. The input data of the artificial neural model, including the length and width of the geographies, the nitrate concentration, and the electrical conductivity value were determined as the output of the model. In order to investigate the performance and efficiency of artificial intelligence models in predicting qualitative information, qualitative information of 50% of the wells was used for calibration and 50% of the wells were used for validating the models. Finally, the output of the models was compared with the value measured in the observation wells based on the mutual error evaluation criteria. The results showed that the ANFIS model performed better than the other two interpolation models in predicting the value of electrical conductivity and nitrate, respectively, with the root mean square error (RMSE) and (mg/l) of 5.362, with the mean bias error (MBE) 2.365 with a correlation coefficient (R) of 0.767. Also, the ANN model had far better results than the usual kriging method. Based on this, ANFIS model is proposed for spatial prediction of electrical conductivity and nitrate in the study area. https://www.pqprc.ir/article_192560_b7995cae2bcf5dbbd4690ce6a54d7f39.pdf