Evaluation of effective parameters of Manning roughness coefficients in HDPE culverts via kernel-based approaches. Issue 6 (23rd July 2022)

• Record Type: Journal Article
• Authors: Nassaji Matin, Ghazaleh
• Is Part Of: Journal of hydroinformatics. Volume 24:Issue 6(2022)
• Page Start: 1111
• Page End: 1126
• Publication Date: 2022-07-23
• Subjects: culvert systems -- Gaussian process regression -- intelligence approaches -- Manning roughness coefficient -- support vector machine; Hydrology -- Data processing -- Periodicals; Geographic information systems -- Periodicals; Geographic information systems; Hydrology -- Data processing; Electronic journals; Periodicals; 551.480285
• DOI: 10.2166/hydro.2022.003
• Languages: English
• ISSNs: 1464-7141

Abstract

Abstract: The prediction of Manning coefficients plays a prominent role in the estimation of head losses along culvert systems. Although the Manning coefficient is treated as a constant, previous studies showed the dependency of this coefficient on several parameters. This study aims to evaluate the effective parameters of the Manning roughness coefficient using intelligence approaches such as Gaussian process regression (GPR) and support vector machines (SVM), in which the input variables were considered as dimensionless and dimensional. In addition to the enhanced efficiency of the SVM approach compared to the GPR approach in model development with dimensionless input variables, the accuracy of model A(I) with input parameters of Fr (Froude) and y / D (the ratio of water depth to culvert diameter) and performance criteria of correlation coefficient ( R ) = 0.738, determination coefficient (DC) = 0.0962, root mean square errors (RMSE) = 0.0015 and R = 0.818, DC = 0.993 and RMSE = 0.0006 for GPR and SVM approaches were the highest. Thus, for the second category, a model with an input parameter of discharge ( Q ), hydraulic radius ( R H ), and culvert's slope ( S 0 ) showed good efficiency in predicting the Manning coefficient, in which the performance criteria of GPR and SVM approaches were ( R = 0.719, DC = 0.949, RMSE = 0.0013) and ( R = 0.742, DC = 0.991, RMSE = 0.007), respectively. Furthermore, developed OAT (one-at-a-time) sensitivity analysis revealed that relative depth y / D and Q are the most important parameters in the prediction of the Manning coefficient for models with dimensionless and dimensional input variables, respectively. HIGHLIGHTS: Effective parameters in predicting the Manning coefficient were evaluated, and the efficiency of GPR and SVM was evaluated in predicting the Manning roughness coefficient of culverts. Although the Manning roughness coefficient is treated as a constant, it was observed that the Manning coefficient depends on several parameters. Results of developed models revealed the uncertainty of friction loss in culvert systems. Graphical Abstract