Application of Whale Optimization Algorithm Combined with Adaptive Neuro-Fuzzy Inference System for Estimating Suspended Sediment Load

Document Type : Regular Article


1 Assistant Professor of Computer Engineering, Faculty of Engineering, University of Bonab, Bonab, Iran

2 Ph.D. Candidate of Hydraulic Structures, Department of Water Engineering, University of Tabriz, Tabriz, Iran


In Iran, no detailed information on the amount of erosion, sediment transport, and sedimentation of rivers, and in many cases, there are many differences between measurements. Since the flow regime and consequently the sediment regime in the drainage basins are not constant, estimation of sediment discharge can help estimate the sediment accumulated behind the water structures, especially the dams, and determining the dead volume of reservoirs in the coming months, and by adopting timely arrangements, the management of discharge will be facilitated to a certain extent during sedimentation. In this study, a hybrid method of the Whale optimization algorithm and the neuro-fuzzy inference system was used to estimate the suspended sediment load (SLL) of the Zarinehrood river. The performance of the proposed methods was evaluated by two statistics, including determination coefficient (R2) and normal root mean square error (NRMSE). SSL of the Zarinehrood river during 10 years with flow discharge was used as inputs. The results showed the high accuracy of the WOA-ANFIS with values R2=0.962 and NRMSE=0.051. In general, a comparison of the results obtained from the hybrid method used in this study showed the high ability and accuracy of the WOA-ANFIS method in estimating the SLL of the Zarinhrood river.

Graphical Abstract

Application of Whale Optimization Algorithm Combined with Adaptive Neuro-Fuzzy Inference System for Estimating Suspended Sediment Load


  • We optimized and estimated suspended sediment load.
  • Whale optimizations meta-heuristic method (WOA) based on the neuro-fuzzy inference system (ANFIS), were used to estimate the suspended sediment load (SLL) of the Zarinehrood river.
  • Discharge in t days (Qrt), suspended sediment load in t days (Srt) were considered as input parameters.
  • Based on the modeling results, the SSL of the Zarinehrood river using the WOA-ANFIS method is predicted with high accuracy and is in good agreement with observed data.
  • Model W3 (Qrt-1, Srt-1Qrt) also models the SSL values with less error according to the input parameters.
  • The results showed the high accuracy of the WOA-ANFIS with values R2=0.962 and NRMSE=0.051.


Main Subjects

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