Estimation of re-aeration coefficient using MLR for modelling water quality of rivers in urban environment

Abstract

Re-aeration is the natural phenomenon responsible for the generation of oxygen through the air water interface, advection, dispersion and transient storage reactions. The rate of re-aeration changes significantly when these reactions get coupled with the wastewater pollutants from domestic, industrial and agricultural discharges. Re-aeration coefficient (K2) is the function of water quality and hydraulic parameters need to be evaluated to predict the water quality. Predictive equations available for the assessment of re-aeration coefficient are based on only hydraulic parameters and are empirically evaluated using local conditions. Therefore, a model is required that is capable of evaluating coefficient based on both the hydraulic and water quality parameters. In this study, multivariate linear regression (MLR) is used for the prediction of re-aeration coefficient in the stretch of river Yamuna. The selected stretch of river extended from North to South in Delhi, India. Five models are designed using different combinations of parameters. The developed models are trained, tested and validated using experimental data of five years. Performance of models were evaluated using coefficient of determination (R2), correlation coefficient (R) and root mean square error (RMSE). Results indicate that the MLR model developed using water quality and hydraulic parameters produces the most accurate correlation between the observed and predicted re-aeration coefficient. Correlation coefficient obtained for the best fit model was 0.896.