Prediction of Groundwater Quality Index in the Selected Divisions of Srikakulam Using Artificial Neural Networks Approach

Abstract

Applicability of artificial neural network (ANN) modelling in predicting the water quality index (WQI) and in turn to ascertain the suitability of the water for human consumption has been presented in the paper. In the light of the present study, seventy-nine (79) groundwater samples were collected from two mandals (divisions) Veeraghattam (VGT) and Palakonda (PLKD) and analyzed for physicochemical parameters during the pre-monsoon and post-monsoon seasons of 2015 and 2016. In computing the WQI, physicochemical parameters such as  pH, EC, TDS, TH, Ca, Mg, chlorine, fluoride, nitrite, DO and TA have been considered. From the results it was found that the WQI varies from 43.9 to 46.5 and 31.4 to 34.7 in VGT and PLKD divisions respectively. ANN tool in MATLAB has been used to predict the WQI. Back propagation methodology and LM algorithm has been chosen for the study. To train the network, physicochemical parameters have been given as inputs and the already computed WQI values as output. A particular season has been chosen for testing the network. After simulating the network, the results obtained were compared with the experimental value and found to have an error of 0.6%. It is inferred that the chosen model fits apt for the prediction of WQI in the present study.