Prediction of the groundwater quality index through machine learning in Western Middle Cheliff plain in North Algeria

Water quality monitoring and assessment has been one of the world's major concerns in recent decades. This study examines the performance of three approaches based on the integration of machine learning and feature extraction techniques to improve water quality prediction in the Western Middle Cheliff plain in Algeria during 2014-2018. The most dominant Water Quality Index parameters that were extracted by neuro-sensitivity analysis (NSA) and principal component analysis (PCA) techniques were used in the multilayer perceptron neural network, support vector regression (SVR) and decision tree regression models. Various combinations of input data were studied and evaluated in terms of prediction performance, using statistical criteria and graphical comparisons. According to the results, the MLPNN1 model with eight input parameters gave the highest performance for both training and validation phases (R = 0.98/0.95, NSE = 0.96/0.88, RMSE = 11.20/15.03, MAE = 7.89/10.22 and GA = 1.34) when compared with the multiple linear regression, TDR and SVR models. Generally, the prediction performance of models integrated with NSA approaches is significantly improved and outperforms models coupled with the PCA dimensionality reduction method.