Development of ANN model for discharge prediction and optimal design of sharp-crested triangular plan form weir for maximum discharge using linked ANN–optimization model

Abstract

Triangular plan form weirs are advantageous over a normal weir in two ways. Within the limited channel width, use of such a weir increases the crest length and hence for a given head, increases the discharge and for a given discharge, reduces the head in comparison with a normal weir. In a previous study, researchers proposed an empirical equation to compute the discharge coefficient of a triangular plan form weir. The prediction error on the discharge coefficient was ±7% from the line of agreement. In the present study, an ANN model has been utilized to train randomly selected 70% data, with 15% tested and validation made for the remaining 15% data. The model predicts the discharge coefficient with a prediction error in the range of ±2.5% from the line of agreement, thereby decreasing the prediction error in Cd by 64%. Also, the sensitivity analysis of the developed ANN model has been performed for all the parameters (weir height, skew weir length and flow depth) involved in the study and the weir height was found to be the most sensitive parameter. Furthermore, the linked ANN–optimization model has been developed to find the optimal values of design parameters of a triangular plan form weir for maximum discharge. HIGHLIGHTS The developed ANN model shows significant improvement in the estimation of discharge coefficient and reduces the prediction error in Cd by 64%.; The ANN model appears to be robust even for large random error levels up to 10% in the input parameters.; Linked ANN–optimization model has been developed to find the optimal values of design parameters of weir L, w and h for which the discharge Q is maximum.;

Keywords

• artificial neural network
• discharge coefficient
• discharge measurement
• optimization
• triangular plan form weir