Discharge estimation in a compound channel with converging and diverging floodplains using ANN–PSO and MARS

Abstract

The discharge estimation in rivers is crucial in implementing flood management techniques and essential flood defence and drainage systems. During the normal flood season, water flows solely in the main channel. During a flood, rivers comprise a main channel and floodplains, collectively called a compound channel. Computing the discharge is challenging in non-prismatic compound channels where the floodplains converge or diverge in a longitudinal direction. Various soft computing techniques have nowadays become popular in the field of water resource engineering to solve these complex problems. This paper uses a hybrid soft computing technique – artificial neural network and particle swarm optimization (ANN–PSO) and multivariate adaptive regression splines (MARS) to model the discharge in non-prismatic compound open channels. The analysis considers nine non-dimensional parameters – bed slope, relative flow depth, relative longitudinal distance, hydraulic radius ratio, angle of convergence or divergence, flow aspect ratio, relative friction factor, and area ratio – as influencing factors. A gamma test is carried out to determine the optimal combination of input variables. The developed MARS model has produced satisfactory results, with a mean absolute percentage error (MAPE) of less than 7% and an R2 value of more than 0.90. HIGHLIGHTS Using traditional methods to estimate discharge in non-prismatic compound channels provides unsatisfactory results.; Discharge is estimated in non-prismatic compound channels using two soft computing techniques ANN–PSO and MARS.; Influencing parameters for the prediction of discharge are identified using the Gamma test.; Different model performances have been carried out for different ranges of width ratio and relative flow depth.;

Keywords

• ann–pso
• gamma test
• mars
• non-prismatic compound channel