Prediction of Nitrate Concentration in Groundwater of the Eastern Region of Mazandaran Province using Soft Computing Algorithms

Considering the importance of fresh water for human life and the vulnerability of groundwater sources to all kinds of pollution and the possibility of transferring pollutants to other surface and groundwater sources, as well as the location of Iran in the arid and semi-arid belt, protecting this valuable and rare element is imperative and its continuous monitoring must be one of the priorities of water resources managers. Therefore, in the current research, nitrate pollution in the eastern plains of Mazandaran province was discussed and relevant issues were investigated, and an efficient and optimal model for predicting nitrate concentration was presented. In this research, three machine learning models including decision tree, logistic regression and artificial neural network were compared. The physical and chemical data measured during the years 1985 to 2020 were used and entered as the input variables of the models. The variables include temperature, water level, pH, EC, HCO3-, CL-, SO24-, Na+, K+, Mg2+, Ca2+, TH and TDS; The amount of nitrate contamination of the groundwater, was predicted by dividing 70% of the dataset as training and 30% as testing data. The R2, RMSE, NSE and PBIAS indexes were applied for model evaluation. The results indicated that the Decision Tree model had the best performance with a large difference compared to the other two models (R2 = 0.957 and RMSE = 0.297, NSE = 0.95 and testing acc = 0.907). After That, logistic regression and artificial neural network had much weaker performances than the lead model. It is suggested to conduct another research with other machine learning models by changing the input variables and add some extra ones such as land-use and compare the results with the current research.