Artificial Neural Network Prediction of Sediment Reduction Ratio in Open Channel Flows
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The ecological equilibrium and structural integrity of open waterways are significantly influenced by the effective management of sediment within hydraulic systems. Although conventional experimental testing methods are precise, they are frequently time-consuming and expensive. This study describes a unique computational technique that uses Artificial Neural Networks (ANNs) to anticipate the Sediment Reduction Ratio (SRR) in open channel flows, offering an alternative to significant physical experiments. The ANN model was trained on a constrained dataset that was obtained from studies that evaluated sediment flow under varied weir heights and channel bed inclinations. The model's performance was rigorously assessed using a variety of metrics, such as the coefficient of determination (R²), coefficient of variation of the root means square error (CVRMSE), mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The model demonstrated exceptional performance, with an MSE of 0.007%, RMSE of 0.87%, MAE of 0.76%, MAPE of 0.84%, CVRMSE of 0.93%, and a R² of 0.99. These results indicate that the ANN is capable of accurately and consistently predicting SRR, thereby offering hydraulic engineers a potent instrument for simulating sediment behavior. This study emphasizes the revolutionary potential of machine learning in environmental engineering, allowing for the creation of more cost-effective and adaptable sediment control systems.
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