Modeling of dissolved oxygen applying stepwise regression and a template-based fuzzy logic system

This paper develops a template-based fuzzy inference system (TFIS) capable of simulating the dissolved oxygen (DO) concentration of an example stream by using daily data (2009-2012). Stepwise regression (SR) analysis and Mallows' Cp statistic were employed to select the best set of independent parameters for the input vector of the TFIS and the regressive models. The most effective inputs were determined for temperature and specific conductivity (P-value=0.000), whereas flow rate (P-value=0.002) and pH (P-value=0.004) were found to be less effective parameters. The TFIS and SR models undersimulated the magnitude of high DO concentrations (>12.5 mg/L), whereas the low and medium DO concentrations were oversimulated. However, they were closer to the observed data. A comparison of the prediction accuracy of the TFIS and SR methods indicated that the TFIS approach was more accurate in simulating DO concentrations, except for low concentrations of DO (<10 mg/L). The TFIS method was found to be superior to the conventional SR model.