Creating a non-linear total sediment load formula using polynomial best subset regression model

Okcu D., Pektas A. O., Uyumaz A.

JOURNAL OF HYDROLOGY, vol.539, pp.662-673, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 539
  • Publication Date: 2016
  • Doi Number: 10.1016/j.jhydrol.2016.04.069
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.662-673
  • Istanbul Technical University Affiliated: Yes


The aim of this study is to derive a new total sediment load formula which is more accurate and which has less application constraints than the well-known formulae of the literature. 5 most known stream power concept sediment formulae which are approved by ASCE are used for benchmarking on a wide range of datasets that includes both field and flume (lab) observations. The dimensionless parameters of these widely used formulae are used as inputs in a new regression approach. The new approach is called Polynomial Best subset regression (PBSR) analysis. The aim of the PBRS analysis is fitting and testing all possible combinations of the input variables and selecting the best subset. Whole the input variables with their second and third powers are included in the regression to test the possible relation between the explanatory variables and the dependent variable. While selecting the best subset a multistep approach is used that depends on significance values and also the multicollinearity degrees of inputs. The new formula is compared to others in a holdout dataset and detailed performance investigations are conducted for field and lab datasets within this holdout data. Different goodness of fit statistics are used as they represent different perspectives of the model accuracy. After the detailed comparisons are carried out we figured out the most accurate equation that is also applicable on both flume and river data. Especially, on field dataset the prediction performance of the proposed formula outperformed the benchmark formulations. (C) 2016 Elsevier B.V. All rights reserved.