Performance Evaluation of Flow over Crump Weir Using Data-Driven Models

Sani Yakubu Khalifa, Babatunde Korode Adeogun, Abubakar Ismail, Morufu Ajibola Ajibike, Muhammad Mujahid Muhammad

Abstract


The flow characteristics changes with varying geometry of hydraulic weir and how the weir is inclined to the direction of flow in a channel. In this way, several studies have investigated the performance weirs experimental with paying much attention on the accurate prediction of discharge coefficient. Thus, the main objective of this study is to employ artificial intelligence (AI) techniques to predict discharge coefficient (Cd) of crump weir models. Hence, the precision and use of seven data-driven models including Bayesian neural network (BNN), multiple linear regression (MLR), multi-layer perceptron neural network (MLPNN), genetic algorithm (GA), support vector machine (SVM), Radial Basis Function (RBF) and curve fitting neural network (CFNN) were examined for estimating of the Cd. To achieve this, experiments were conducted on eighteen crump weir models of different apex angles 80o, 90o 100o, 110o 120o and 130°. The upstream angles of the weir models were set in decreasing order of 85o, 70o, 55o, 40o, 25o and 10o. While the downstream angles were increased as 15o, 20o, 25o, 30o, 35o and 40o respectively. 360 laboratory test results were used, 70% for training, 15% for testing and 15% for validation. And statistical parameters of coefficient of determination (R2), root-mean-square error (RMSE), mean absolute error (MAE), were employed as the criteria for the comparison of the models' performance. Results showed good agreements between the observed and estimated values using the AI-based models. However, among these models, the CFNN managed to estimate the Cd of the weir with the highest precision and accuracy than the rest of the models with (RMSE=0.1635x10-4, R2=0.9981,). Also, it was found that the most efficient crump was weir model 17 for having the least Cd of 1.14914 and least percentage error of 12.97412, which has been optimized using GA with Cd value of 1.14815. 


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