Open AccessAssessment of river water quality indices based on various fuzzy models and arithmetic indexing method
Author(s) -
A. Divya,
P A Soloman
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1114/1/012092
Subject(s) - mean squared error , water quality , mathematics , statistics , approximation error , fuzzy logic , hydrology (agriculture) , sigmoid function , index (typography) , environmental science , computer science , artificial neural network , machine learning , artificial intelligence , geology , geotechnical engineering , ecology , world wide web , biology
River water is a major source of natural freshwater for both rural and urban areas. The severe threat in the form of pollutants is the major cause of deterioration of surface water quality. Water Quality Index (WQI) is a single measure of overall water quality in a specific location with a special emphasis on the time-based readings of water quality parameters. WQI can be used as a good tool to assess the intensity of water pollution. In this study, two water quality index models using fuzzy logic in MATLAB R2015a by trapezoidal and sigmoid membership functions are proposed. Fuzzy water quality index models were developed for various seasons, using eight experimentally estimated water quality parameters, such as Temperature (T), Chlorides (Cal − ), Nitrates (NO 3 − ), Sulphates (SO 4 − ), Total Coli forms (TC), Total Dissolved Solids(TDS), Electrical conductivity (EC) and Total Hardness (TH) of the water samples at 8 locations stretching55 km of Chalakudy River from January 2018 to December 2018. The models are validated by comparing with the WQI values obtained by the application of arithmetic index method (AIM) based on Indian Standards, by finding the absolute average relative error (AARE) and root mean square error (RMSE). The fuzzy logic model using trapezoidal membership function (FTWQI) was found to be more reliable with the least error (AARE 0.0214 and RMSE 0.318) compared with the fFuzzy sigmoid water quality index (FSWQI) (AARE 0.573 and RMSE 0.86). The models enable the easy prediction of the risk of water consumption.