Abstract
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
Weirs are widely used as flow diversion and flow measuring devices in irrigation engineering
The developed artificial neural networks (ANNs) model shows significant improvement in the estimation of discharge coefficient and reduces the prediction error in Cd by 64%
The following are the highlights of this study: 1. The developed ANN model shows significant improvement in the estimation of discharge coefficient and reduces the prediction error in Cd by 64%
Summary
Weirs are widely used as flow diversion and flow measuring devices in irrigation engineering. The problem statement of this study is to develop an ANN model to improve the predicted discharge coefficient of a sharp-crested triangular plan form weir which is already predicted by Kumar et al ( ). Since the non-dimensional input and output parameters have been used in the proposed ANN model, it will be applicable to all sharpcrested triangular plan form weirs which are used to measure real-time data. The universal approximating capability of ANN models has been utilized in this study to estimate the discharge coefficient of a sharp-crested triangular plan form weir. Different values of weir heights (w), weir lengths (L) and flow depths (h) are used as input while the corresponding discharge coefficients (Cd) are used as output of the ANN model. The weight matrices (W1, W2, B1 and B2) obtained after the training for connections between different layers are given below:
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