University of Qom Civil Infrastructure Researches 2783-140X 9 1 2023 08 23 Estimation of the Downstream Scour Depth of Vertical Drop Using the Support Vector Machine (SVM) Algorithm Estimation of the Downstream Scour Depth of Vertical Drop Using the Support Vector Machine (SVM) Algorithm 1 11 2264 10.22091/cer.2022.8208.1395 FA Hossein Mohammadnezhad Department of Civil Engineering, Faculty of Engineering, Urmia University, Urmia, Iran. Mirali Mohammadi Department of Civil Engineering, Faculty of Engineering, Urmia University, Urmia, Iran. 0000-0001-7194-9393 Mohammad Bagherzadeh Department of Civil Engineering, Faculty of Engineering, Urmia University, Urmia, Iran. 0000-0002-5837-3331 Journal Article 2022 05 13 The downstream scour of the vertical drop can be one of the causes of instability and failure of this structure. In the present study, the downstream scour depth of this structure predicted using the support vector machine (SVM) method. For this purpose, 104 experimental data used to estimate the scour depth. hese data are a function of the two dimensionless parameters of dansimetric Froude number (Frj) and tailwater depth (yt / yj) that have been entered into the SVM in three different models. To evaluate the results, the evaluation criteria of R2, NRMSE, DC, and MARE used. The results showed that model number (1) with the input combination (Frj and yt / yj) with R2 = 0.9777, DC = 0.929, NRMSE = 0.0775, and MARE = 11.89% for the test stage leads to the best result. The SVM method also has appropriate accuracy, acceptable results, and desirable performance in estimating the scour depth. Also, it was found that the densimetric froude number has a greater effect on estimating the relative scour depth compared to the tailwater depth. The downstream scour of the vertical drop can be one of the causes of instability and failure of this structure. In the present study, the downstream scour depth of this structure predicted using the support vector machine (SVM) method. For this purpose, 104 experimental data used to estimate the scour depth. hese data are a function of the two dimensionless parameters of dansimetric Froude number (Frj) and tailwater depth (yt / yj) that have been entered into the SVM in three different models. To evaluate the results, the evaluation criteria of R2, NRMSE, DC, and MARE used. The results showed that model number (1) with the input combination (Frj and yt / yj) with R2 = 0.9777, DC = 0.929, NRMSE = 0.0775, and MARE = 11.89% for the test stage leads to the best result. The SVM method also has appropriate accuracy, acceptable results, and desirable performance in estimating the scour depth. Also, it was found that the densimetric froude number has a greater effect on estimating the relative scour depth compared to the tailwater depth. https://cer.qom.ac.ir/article_2264_af92f31e0d6f8b05b498cf609841f552.pdf