Suspended sediment load modeling using advanced hybrid rotation forest based elastic network approach

Khosravi, Khabat and Golkarian, Ali and Melesse, Assefa M. and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 (2022) Suspended sediment load modeling using advanced hybrid rotation forest based elastic network approach. Journal of Hydrology, 610:127963. pp. 1-14. ISSN 0022-1694


Abstract

The distribution and transportation of suspended sediment load (Qssl) in rivers have a significant effect on the design of hydraulic structures, river morphology, water quality, and aquatic ecosystems. As direct measurement of Qssl can be costly and time-consuming, reliable estimates are vital for watershed management. In the present study, four standalone models including an Elastic Network (EN), Alternating Model Tree (AM Tree), Reduced Error Pruning Tree (REP Tree) and the Dual Perturb and Combine Tree (DPC Tree), including four hybridized models that combine a standalone model with the Rotation Forest (RF), were developed and evaluated for Qssl prediction at Talar Watershed, in the northern Iran. Multiple scenarios comprised of antecedent flow discharge (Qw), rainfall (R) and Qssl, with all constructed manually and automatically using a Wrapper Feature Selection (WFS) to predict the Qssl at Shirgah hydrometric station from January 1, 2004 to September 22, 2019. The optimal model fitted results were evaluated using multiple graphical and quantitative metrics with the results revealing that the flow discharge is perhaps the best predictor of Qssl. Based on the Nash-Sutcliffe Efficiency (NSE) metric, the RF-EN (NSE = 0.85), EN (NSE = 0.83), AM Tree (NSE = 0.79), RF-AM Tree (NSE = 0.81) and the RF-REP Tree (NSE = 0.79) models seemed to perform very well, with the REP Tree (NSE = 0.65) and RF-DPC (NSE = 0.71) performed well, while the DPC Tree model (NSE = 0.35) were notably unsatisfactory. The hybridized models, however, captured extreme values more accurately compared with the standalone models. Finally, the model outputs were compared to the well-known optimized ANFIS models with a metaheuristic approach (imperialist competitive algorithm (ICA) and BAT algorithms), and all these results revealed that most the of newly developed models outperformed the ANFIS-ICA and ANFIS-BAT algorithms. The new modelling approaches developed and testing using advanced hybrid Rotation Forest based Elastic Networks in this study have important practical implications for suspended sediment load modeling and applications.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Date Deposited: 07 Jun 2022 01:05
Last Modified: 07 Jun 2022 01:05
Uncontrolled Keywords: Suspended sediment load; Input scenario; Wrapper feature selection; Machine learning; Hybrid algorithm; Talar watershed
Fields of Research (2020): 37 EARTH SCIENCES > 3707 Hydrology > 370704 Surface water hydrology
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280111 Expanding knowledge in the environmental sciences
Identification Number or DOI: https://doi.org/10.1016/j.jhydrol.2022.127963
URI: http://eprints.usq.edu.au/id/eprint/48724

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