Analysis and modelling of surface water quality in river basins

Abstract

Water is one of the prime elements responsible for life on the earth. India’s surface water flows through 14 major river basins beyond innumerable medium/minor basins. The climate change is affecting the precipitation and ultimately affects the quantity of freshwater available, whereas, increasing waste water loads from point and non-point sources are deteriorating the quality of surface wateras well as ground water resources. The surface water
quality is a very important and sensitive issue and is a great environmental concern worldwide. Surface water pollution by chemical, physical, microbial and biological
contaminants can be considered as an epidemic all over the world. The Study area of research work is Brahmani River Basin in Odisha, India. The monthly water quality parameters are collected and analyzed from five selected gauging stations of Odisha during the months of January to December from 2003 to 2012. Eleven physical, chemical and biological water quality parameters viz.,pH, Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Electrical Conductivity, Nitrogen as nitrate (Nitrate-N), Total Coli-form Bacteria(TC), Fecal Coli-form Bacteria(FC), Chemical Oxygen Demand (COD), Nitrogen as ammonia (NH4-N),
Total Alkali (TA) as CaCO3, Total Hardness (TH) as CaCO3 are selected for the analysis. Analysis of water quality for Brahmani River is done by techniques such as Spearman’s Rank Correlation, Calculation of parts of water quality parameter, Overall Water Quality Index(WQI), Multivariate Analysis of variance (MANOVA) with Discriminant Analysis, Principal component Analysis and Factor Analysis, Canonical Correlation Analysis (CCA), Cluster Analysis (CA). Modelling is done by using Adaptive Neuro-Fuzzy Inference System (ANFIS) in MATLAB, Artificial Neural Network (ANN) and risk based analysis by Monte Carlo simulations (MCS). The Error analysis and performance evaluation of the applied
models were also done to know the best fit model for the study. Regression plots between actual and predicted WQI via ANFIS revealed high values of coefficient of determination(R2) of 0.994 and 0.995 for training and testing in summer season, 0.985 and 0.990 in monsoon season and 0.992 and 0.993 in winter season respectively. However, the coefficients of determination (R2) for Artificial Neural Network (ANN) between actual and predicted values of WQI were 0.945, 0.941 and 0.965 for summer, monsoon and winter seasons respectively. Monte Carlo Simulations (MCSs) provide techniques for simulating the parameters having high degrees of freedom. There is least error in case of ANFIS when compared with ANN and MCS. Therefore, it can be stated that ANFIS predicted WQI with a far better accuracy than ANN and MCS. From the results of ANFIS, it can be concluded that if the present conditions can be considered to remain the future years could have most likely similar trend as from the trend observed during2003 to 2012.