Modeling of Dissolved Oxygen in River Water Using Artificial Intelligence Techniques

Abstract

The accuracy of artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP) in modeling dissolved oxygen (DO) concentration was investigated in this study. Water temperature, specific conductance, pH, discharge and DO concentration data from South Platte River at Englewood, Colorado were used. Various input combinations of these data were tried as inputs to the ANN and ANFIS methods. The ANN and ANFIS models with the water temperature, specific conductance, pH and discharge input parameters performed the best. The optimal GEP model was obtained for the best input combination and compared with the ANN and ANFIS models with respect to correlation coefficient, root mean square error, mean absolute error and mean absolute relative error criteria. Results revealed that the GEP model performed better than the ANN and ANFIS models in modeling DO concentration.

Keywords: dissolved oxygen, modeling, neural networks, neuro-fuzzy, gene expression programming