The computation of total flow in a flooded river is very crucial work in designing economical flood defense schemes and drainage systems. Further, under non-uniform flow conditions like in converging and diverging compound channel, the traditional methods provide poor results with high errors. The analytical methods require the system of nonlinear equations to be solved, which is very complex. So, mathematical models that prompt in taking care of a complex system of problems are solved here through an artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). By utilizing ANN and ANFIS, an attempt is made to predict the discharge in converging and diverging compound channels. In the analysis, the most influencing dimensionless parameters such as friction factor ratio, area ratio, hydraulic radius ratio, bed slope, width ratio, relative flow depth, angle of converging or diverging, relative longitudinal distance, and flow aspect ratio are taken into consideration for computation of discharge. Gamma test and M-test have been performed to achieve the best combinations of input parameters and training length respectively. The significant input parameters that influence the discharge are found to be friction factor ratio, hydraulic radius ratio, relative flow depth, and bed slope. A suitable performance is achieved by the ANFIS model as compared to ANN model with a high coefficient of determination of 0.86 and low root mean square error of 0.005 in predicting the discharge of non-prismatic compound channels taken under consideration.
Adaptive Neuro-Fuzzy Inference System of friction factor and heat transfer nanofluid turbulent flow in a heated tube
