Estimation of transverse mixing coefficient in streams using M5, MARS, GA, and PSO approaches

Transverse mixing coefficient is one of the key elements of pollutant in two-dimensional modeling. In this study, four data-driven models, M5 model tree, multivariate adaptive regression splines (MARS), genetic algorithm (GA), and particle swarm optimization (PSO), were used to estimate the transverse mixing coefficient. For this purpose, techniques with a wide range of experimental and field data were performed to train and test the data-driven models. Statistical analysis and Monte Carlo simulation were used to validate the performance of each model. Based on statistical indices, the efficiency of M5 and MARS models was better than GA and PSO algorithms. In straight streams, M5 and MARS provided similar performances, but the MARS model estimated the transverse mixing coefficient more accurately in meander streams. In meander streams, the performance of all models was lower than straight streams due to the lack of experimental and field measurements for large meandering streams. Applying Monte Carlo simulation showed that the MARS model overestimated the transverse mixing coefficient in many cases. In addition, the results of global sensitivity analysis showed that 70% of output variance was determined by main effects in the M5 model and 30% by interaction effects. In this regard, the most influential parameters were flow depth and shear velocity, while the average velocity was the least influential factor.