Evaluation of the performance of machine learning methods for estimating the maximum scour depth around the bandallike spur-daike

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Document Type : Research Paper

Authors

1 Department of Hydraulic Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

2 Department of Hydrology and Water Resources , Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

Abstract

In this study, the performance of machine learning based methods for predicting the maximum scour depth around a Bandallike spur-dike is evaluated. For this purpose, three methods of Random Forest(RF model, Support Vector Machine(SVM) and Gene Expression Programming(GEP) were used.To train and test the models, 108 data series(87 series for training and 21 series for testing) were extracted from the results of an experimental study.The models were evaluated with four different combinations of inputs (Fr: flow Froude number, S/L: ratio of distance to breakwater length, θ: spur-dike installation angle relative to the bank, and α: porosity of the permeable structure). The results showed that for all methods in the one input mode, the parameters with themost and least impact were,in order, α and S/L. In the SVM model,the average MAE index increased by about 2 times when the number of inputs increased from one input mode. In the GEP model, the average MAE index increased by about 3.5 times when the number of inputs increased from three to four inputs mode. However, in the RF method, increasing the number of inputs led to an increase in model accuracy, and the average MAE index decreased by 83% in the four inputs mode compared to the three inputs mode. Finally, it was found that the RF method had much better performance (MAE = 0.006 and RMSE = 0.009) in estimating the scour depth around the Bandal-like spur-dike than the other methods, and this model had less error spread with the same inputs.

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Iranian Journal of Soil and Water Research

Articles in Press, Accepted Manuscript
Available Online from 29 May 2024

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