Inference of Water Quality Index Using ANFIA and PCA

Abstract

River Brahmani is reported to be polluted from the effluents discharged from the nearby industries, towns and villages located near the banks. The presence of heavy metal content and radioactive material makes it most unsuitable for human use. The fertilizers used for agricultural purpose affect the pH and nitrate content of water. Evaluation of Water Quality Index (WQI) of water is extremely important in the gauging stations located near the industries to prepare remedial measures. To this end, the present study proposes an efficient methodology such as adaptive Neuro fuzzy inference system (ANFIS) for the prediction of water quality in Brahmani River. The water quality parameters used to assess are usually inter correlated with each other and this makes an assessment unreasonable. Therefore, the parameters are uncorrelated using principal component analysis with varimax rotation. The uncorrelated values are fuzzified to take into account uncertainty and impreciseness during data collection and application in ANFIS. An efficient rule base and optimal distribution of membership function is constructed from the hybrid learning algorithm of ANFIS in MATLAB. The model performed quite satisfactory with actual and predicted data on water quality.