{"title":"Approximation in scour depth around spur dikes using novel hybrid ensemble data-driven model.","authors":"Balraj Singh, Vijay K Minocha","doi":"10.2166/wst.2024.025","DOIUrl":null,"url":null,"abstract":"<p><p>The scouring process near spur dikes poses a threat to riverbank stability, making it crucial for river engineering to accurately calculate the maximum scour depth. However, determining the maximum scour depth has been challenging due to the intricacy of scour phenomena surrounding these structures. This research introduces a reliable ensemble data-driven model by hybridizing random tree (RT) using additive regression (AR), bagging (B), and random subspace (RSS) for predicting scour depths around spur dikes. A database of 154 experimental observations was collected from literature, with 103 and 51 observations used for training and testing subsets, respectively. A dimensionless analysis was performed on the collected dataset, selecting four variables as input variables (v/v<sub>s</sub>, y/l, l/d<sub>50</sub>, and Fd<sub>50</sub>) and d<sub>s</sub>/l as response variables. The performance comparison demonstrates that B_AR_RT has a better coefficient of determination (R<sup>2</sup>) of 0.9693, root mean square error (RMSE) of 0.1305, and Nash-Sutcliffe efficiency (NSE) of 0.9692. Finally, a comparison of the best hybrid model has been done with previous studies, and sensitivity analysis is performed to determine the most influential parameter for predicting the scour depth around spur dikes.</p>","PeriodicalId":23653,"journal":{"name":"Water Science and Technology","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/wst_2024_025/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water Science and Technology","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.2166/wst.2024.025","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
The scouring process near spur dikes poses a threat to riverbank stability, making it crucial for river engineering to accurately calculate the maximum scour depth. However, determining the maximum scour depth has been challenging due to the intricacy of scour phenomena surrounding these structures. This research introduces a reliable ensemble data-driven model by hybridizing random tree (RT) using additive regression (AR), bagging (B), and random subspace (RSS) for predicting scour depths around spur dikes. A database of 154 experimental observations was collected from literature, with 103 and 51 observations used for training and testing subsets, respectively. A dimensionless analysis was performed on the collected dataset, selecting four variables as input variables (v/vs, y/l, l/d50, and Fd50) and ds/l as response variables. The performance comparison demonstrates that B_AR_RT has a better coefficient of determination (R2) of 0.9693, root mean square error (RMSE) of 0.1305, and Nash-Sutcliffe efficiency (NSE) of 0.9692. Finally, a comparison of the best hybrid model has been done with previous studies, and sensitivity analysis is performed to determine the most influential parameter for predicting the scour depth around spur dikes.
期刊介绍:
Water Science and Technology publishes peer-reviewed papers on all aspects of the science and technology of water and wastewater. Papers are selected by a rigorous peer review procedure with the aim of rapid and wide dissemination of research results, development and application of new techniques, and related managerial and policy issues. Scientists, engineers, consultants, managers and policy-makers will find this journal essential as a permanent record of progress of research activities and their practical applications.