Estimation of water's surface elevation in compound channels with converging and diverging floodplains using soft computing techniques

Abstract

In this research, the water level (surface profile) in compound channels with convergent and divergent floodplains using soft computing models including the Multi-Layer Artificial Neural Network (MLPNN), Group Method of Data Handling (GMDH), Neuro-Fuzzy Group Method of Data Handling (NF-GMDH) and Support Vector Machine (SVM) was modeled and predicted. For this purpose, laboratory data published in this field were used. Parameters including convergence angle (with a positive sign) and divergence angle (with a negative sign), relative depth, and relative distance were used as input variables. The results showed that all the used models have appropriate performance, the best performance was related to the support vector machine model with statistical indicators of R² = 0.998 and RMSE = 0.008 in the test stage. The use of the adaptive fuzzy approach in the development of the GMDH model led to a remarkable increase in this model and reached the values of statistical indicators R² = 0.985 and RMSE = 0.203 in the test stage. It was found that the best performance of the activator and kernel functions in the development of the artificial neural network model and the support vector machine is related to the sigmoid and radial tangent functions (kernel).