Application of a genetic algorithm in predicting the percentage of shear force carried by walls in smooth rectangular channels

Shear stress comprises basic information for predicting average depth velocity and discharge in channels. With knowledge of the percentage of shear force carried by walls (%SFw) it is possible to more accurately estimate shear stress values. The %SFw in smooth rectangular channels was predicted by extending two soft computing methods: Genetic Algorithm Artificial (GAA) neural network and Genetic Programming (GP). In order to investigate the percentage of shear force, 8 data series with a total of 69 different data were used. The outcomes of the GAA model (an equation) and the GP model (a program) were presented. In order to detect these models' ability to predict %SFw, the obtained results were compared with several equations derived by other researchers. The GAA model with RMSE of 2.5454 and the GP model with RMSE of 3.0559 performed better than other equations with mean RMSE of about 9.630.