Exploring Artificial Intelligence Techniques for Groundwater Quality Assessment

Freshwater quality and quantity are some of the fundamental requirements for sustaining human life civilization. The Water Quality Index is most extensively used parameter determining water worldwide. However, traditional approach calculation WQI often complex time consuming since it requires handling large data sets involves several subindices. We investigated performance artificial intelligence techniques, including particle swarm optimization (PSO), a naive Bayes classifier (NBC), support vector machine (SVM), predicting index. an SVM NBC prediction, in conjunction with PSO optimization. To validate obtained results, groundwater parameters their corresponding indices were found collected from Pindrawan tank area Chhattisgarh, India. Our results show that PSO–NBC provided 92.8% prediction accuracy indices, whereas PSO–SVM was 77.60%. study’s outcomes further suggest ensemble learning (ML) algorithms can be to estimate predict significant accuracy. Thus, proposed framework directly using measured field while saving effort.