Enhanced Artificial Neural Network with Harris Hawks Optimization for Predicting Scour Depth Downstream of Ski-Jump Spillway

Abstract

A spillway is a structure used to regulate the discharge flowing from hydraulic structures such as a dam. It also helps to dissipate the excess energy of water through the still basins. Therefore, it has a significant effect on the safety of the dam. One of the most serious problems that may be happening below the spillway is bed scouring, which leads to soil erosion and spillway failure. This will happen due to the high flow velocity on the spillway. In this study, an alternative to the conventional methods was employed to predict scour depth (SD) downstream of the ski-jump spillway. A novel optimization algorithm, namely, Harris hawks optimization (HHO), was proposed to enhance the performance of an artificial neural network (ANN) to predict the SD. The performance of the new hybrid ANN-HHO model was compared with two hybrid models, namely, the particle swarm optimization with ANN (ANN-PSO) model and the genetic algorithm with ANN (ANN-GA) model to illustrate the efficiency of ANN-HHO. Additionally, the results of the three hybrid models were compared with the traditional ANN and the empirical Wu model (WM) through performance metrics, viz., mean absolute error (MAE), root mean square error (RMSE), coefficient of correlation (CC), Willmott index (WI), mean absolute percentage error (MAPE), and through graphical interpretation (line, scatter, and box plots, and Taylor diagram). Results of the analysis revealed that the ANN-HHO model (MAE = 0.1760 m, RMSE = 0.2538 m) outperformed ANN-PSO (MAE = 0.2094 m, RMSE = 0.2891 m), ANN-GA (MAE = 0.2178 m, RMSE = 0.2981 m), ANN (MAE = 0.2494 m, RMSE = 0.3152 m) and WM (MAE = 0.1868 m, RMSE = 0.2701 m) models in the testing period. Besides, graphical inspection displays better accuracy of the ANN-HHO model than ANN-PSO, ANN-GA, ANN, and WM models for prediction of SD around the ski-jump spillway.

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