@article { author = {Samadi, M. and Jabbar, E.}, title = {Assessment of Regression Trees and Multivariate Adaptive Regression Splines for Prediction of Scour Depth Below the Ski-Jump Bucket Spillway}, journal = {Journal of Hydraulics}, volume = {7}, number = {3}, pages = {73-79}, year = {2012}, publisher = {Iranian Hydraulic Association}, issn = {2345-4237}, eissn = {2645-8063}, doi = {10.30482/jhyd.2012.85350}, abstract = {Spillways are constructed in dams in order to discharge the excess water in the reservoir. In the skijumpbucket spillways, water jet impacts diagonally to downstream erodible bed and causes scourhole downstream of the dam. The scour hole development may threaten the stability of the dam.Hence, an accurate and correct estimation of scour depth is one of the most important issues inhydraulic engineering. In recent years, soft computing tools have been widely used to model complexand nonlinear phenomena. Therefore, in this study, using data mining algorithms such asclassification and regression trees and multivariate adaptive regression splines have been used forestimation of maximum scour depth at the downstream of the ski-jump bucket spillway. For thispurpose, these models were developed using 95 experimental data and dimensionless parameters. Theresults showed 3 q gH as the most important parameter in prediction of scour depth. In addition,statistical indicators and scatter diagrams showed that multivariate adaptive regression splines havethe highest value of correlation coefficient CC=0.966 and minimum error measures RMSE=0.075 andMAE=0.057 and were more accurate than regression trees in prediction of scour depth below a skijumpbucket spillway.}, keywords = {}, title_fa = {ارزیابی درختان رگرسیونی و رگرسیون تطبیقی چندمتغیره اسپلاین در تخمین حداکثر عمق آب شستگی در پایین دست سرریز جامی شکل}, abstract_fa = {}, keywords_fa = {}, url = {https://jhyd.iha.ir/article_85350.html}, eprint = {https://jhyd.iha.ir/article_85350_d764f622c8ab517b24f924bd823f4d8e.pdf} }