Evaluation of ground water quality contaminants using linear regression and artificial neural network models

Contamination of groundwater is one of the major health concerns in the rapidly urbanizing and industrializing world. Since groundwater is one of the most important resources for the domestic, industrial, and agricultural purposes, the quality and quantity is of prime importance. Nitrite which is a reduced form of nitrate ion is one of the potential contaminants in the groundwater. The detection of nitrite ion is one of the laborious works and also it gets easily oxidized to nitrate ion and hence modeling approaches for the nitrite concentration will be one of the resilient quantification techniques. In the present study, the effective performance of the linear and non-linear models such as multiple linear regression (MLR), principal component regression (PCR), artificial neural network (ANN), and the integrated technique of principal components and artificial neural network (PC-ANN) is evaluated in the prediction of the nitrite concentration. The MLR and PCR showed better results either in generation step or in the validation step but not both. ANN shows better results in both generation and validation steps but the results in the validation steps, though good but accuracy is comparatively lower than the generation step. In the case of PC-ANN, the prediction of the model is found to be good both in the generation and in the validation steps. The Nash-Sutcliffe efficiency test clearly illustrates better performance of PC-ANN in comparison with other models in the present study for the quantification of nitrite concentration in groundwater.