Classification and Prediction of Developed Water Quality Indexes Using Soft Computing Tools

Abstract

The water quality index (WQI) is a coherent method of expressing the state of the water quality, which has become complicated due to subjectivity and ambiguity in the data. The proposed study aims to classify and predict the water quality for potable water by applying soft computing techniques, namely fuzzy, adaptive-network-based fuzzy inference system (ANFIS) and artificial neural network (ANN), along with a novel weight-integrated health hazard index (HI). Initially, the water classification was performed through the fuzzy and HI indexes and was subsequently used for predictive modelling through the ANFIS and ANN on the 349 water samples for total dissolved solids (TDS), chloride (Cl−), hardness (as CaCO3), fluoride (F−), nitrates (NO3−), iron (Fe), and copper (Cu) parameters collected from the municipality region of Jaipur, India. The trained ANFIS model was proven satisfactory for fuzzy and HI indexes with the coefficient of determination (R2) value of 0.8413 and 0.996, respectively. In contrast, the ANN model failed to provide an adequate result for the fuzzy index with an R2 value of 0.5243 and a satisfactory result for HI with an R2 value of 0.839. The study’s novelty lies in predicting the water quality index using ANFIS and ANN for the developed unique HI and fuzzy-based indexes. The results of this study prove that ANFIS is a trustworthy and reliable approach for predicting WQI for potable water, which serves as a valuable guide for decision-makers in the field of water resource management.