Comparison of the gene expression programming, artificial neural network (ANN), and equivalent Muskingum inflow models in the flood routing of multiple branched rivers

Abstract

Floods are considered as natural hazards. The parameter estimation of hydrologic methods is time-consuming in the flood routing of multiple inflows river systems. This paper presents the application of gene expression programming (GEP) and artificial neural network (ANN) as alternative approaches to predict the outflow hydrograph in downstream of multiple inflows systems. GEP and ANN models were compared with the equivalent Muskingum inflow model. The Gharesoo River Basin as a multiple inflows river system was applied for the calibration and verification phases of these models. The GEP obtained the formula as a function of the inflow branches based on the fitting data for simulating the outflow hydrograph of the multiple inflows system. GEP and ANN models investigated inflow hydrographs at different time steps. The obtained outflow hydrograph by the GEP model indicated an excellent performance compared with ANN and equivalent Muskingum inflow models in the case involving multiple inflows system.