Optimizing water quality indexing using the adaptive neuro-fuzzy inference system for cost-effective enhanced monitoring and pollution control

In order to maintain aquatic ecosystems, ensure safe drinking water, and manage water resources sustainably, water quality monitoring is crucial.Traditional Water Quality Index tools often overlook complex non-linear relationships between different water quality parameters, while few can be adjusted to different contexts.This paper suggests an Adaptive Neuro-Fuzzy Inference System based optimised Water Quality Index model to improve monitoring accuracy and lower associated costs.Fuzzy logic and neural networks are combined in the Adaptive Neuro-Fuzzy Inference System model to provide a reliable, affordable framework for more accurate water quality assessment.The proposed approach not only improves the accuracy of water quality assessments but also optimises resource allocation for pollution control.Adaptive Neuro-Fuzzy Inference System based strategy performs better than traditional techniques and provides a more accurate dependable and economical tool for managing water quality.