Prediction of water quality index using artificial neural network and multiple linear regression modelling approach in Shivganga River basin, India

Abstract

The present study has applied artificial neural network (ANN) and multiple linear regression (MLR) techniques to predict the fitness of groundwater quality for drinking from Shivganga River basin, located on the eastern slopes of the Western Ghat region of India. In view of this, thirty-four (34) representative groundwater samples have been collected and analyzed for major cations and anions during pre- and post-monsoon seasons of 2015. The physicochemical parameters such as pH, EC, TDS, TH, Ca, Mg, Na, K, Cl, HCO3, SO4, NO3 and PO4 were considered for computing water quality index (WQI). Analytical results confirmed that all the parameters are within acceptable range; however, EC, TDS, TH, Ca and Mg are exceeding the desirable limit of the WHO drinking standards. The groundwater suitability for drinking was ascertained by WQI method. The WQI value ranges from 25.75 to 129.07 and from 37.54 to 91.38 in pre- and post-monsoon seasons, respectively. Only one sample (DW5) shows 129.07 WQI value indicating poor quality for drinking due to input of domestic and agricultural waste. In the view of generating consistent and precise model for prediction of WQI-based groundwater quality, a Levenberg–Marquardt three-layer back propagation algorithm was used in ANN architecture. Further, MLR model is used to check the efficiency of ANN prediction. The results corroborated that predictions of ANN model are satisfactory and confirms consistently acceptable performance for both the seasons. The proposed ANN model may be useful in similar studies of groundwater quality prediction for drinking purpose.