• Arunkumar T D
• B E Yogendra
• Prashanth J
• Prema N S

In order to keep aquatic ecosystems healthy and ensure that human use of water is safe, water quality testing is crucial. A popular metric for evaluating the general quality of water based on numerous physicochemical factors is the Water Quality Index (WQI). Making proactive decisions and managing water quality can benefit from accurate WQI value predictions. In this study, a dataset of pertinent physicochemical parameters is used to train an Adaptive Neuro-Fuzzy Inference System (ANFIS) model to forecast WQI values. ANFIS is a hybrid computational approach that combines the interpretability of fuzzy logic and the adaptability of neural networks. The dataset comprises of historical data on the quality of the water, including essential metrics like pH, dissolved oxygen, and turbidity.

"Prediction of ANFIS Model Implemented On WQI Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.110-124, March-2019, Available :http://www.jetir.org/papers/JETIR1903P16.pdf

"Prediction of ANFIS Model Implemented On WQI Data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp110-124, March-2019, Available at : http://www.jetir.org/papers/JETIR1903P16.pdf