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

The distribution and transportation of suspended sediment load (Q(ssl)) 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 Q(ssl) prediction at Talar Watershed, in the northern Iran. Multiple scenarios comprised of antecedent flow discharge (Q(w)), rainfall (R) and Q(ssl), with all constructed manually and automatically using a Wrapper Feature Selection (WFS) to predict the Q(ssl) 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 Q(ssl). 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.

Automated summary

In this study, multiple standalone and hybrid models were developed to predict suspended sediment load in rivers, and the models were evaluated using various metrics. The results showed that flow discharge is the best predictor of suspended sediment load. The hybrid models performed better in capturing extreme values compared to the standalone models.