Irrigation Sciences and Engineering

Irrigation Sciences and Engineering

Suspended sediment load estimation using Support Vector Machine-Simulated Annealing algorithm and improved computational methods

Document Type : Research Paper

Authors
Hamed Nozari 1
Fatemeh Chodani 2
Mahdi Kalantari 3
Alireza Mehrabani Bashar 2
1 Associate Professor, Dapartment of Water Sciences and Engineering, Bu-Ali Sina University, Hamedan, Iran
2 M.Sc. graduated, Department of Water Sciences and Engineering, Bu-Ali Sina University, Hamedan, Iran.
3 Assistant Proffesor, Department of Statistics, Payame Noor University, Tehran, Iran.
10.22055/jise.2024.44894.2096
Abstract
Studying and developing water resource systems requires an accurate prediction of river suspended sediment load. Since sediment load measurement is costly and time-consuming, the simulation methods based on the sediment load time series are usually restricted to sample numbers. This study used two different methods to forecast the monthly suspended sediment load. In the first method, the Support Vector Machine (SVM) algorithm was used to forecast sediment load based on a monthly time series of suspended sediment data. In this method, the hyperparameter coefficients of the SVM model were optimized using the Simulated Annealing (SA) algorithm. Additionally, this model was evaluated under single-input-single-output (SISO) and multiple-input-single-output (MISO) patterns. The suspended sediment load and flow data of the Yalfan River from 2007 to 2015 were the SISO’s inputs and outputs, respectively. Moreover, the MISO’s second input was a fixed number series. In the second method, the sediment rating curve and the FAO method forecast it. A %9.93-point increase in the Standard Error (SE) index was obtained by normalizing the SVM-SA’s input data. Switching the input patterns from single input-single output (SISO) to multiple input-single output (MISO) resulted in a %1.03 improvement in SE. By optimizing the coefficients of the discharge-sediment variables in the rating curve and FAO method using the General Reduced Gradient (GRG) technique, the SE index improved by %10.13 and %73.19, respectively. The results showed that the SVM-SA model based on the normalized MISO approach is the best method for suspended sediment load forecasting.
Keywords
Sediment Rating Curve
FAO Method
Generalized Reduced Gradient
Nonlinear Optimization
Subjects
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Irrigation Sciences and Engineering
Volume 48, Issue 3
Autumn 2025
Pages 75-93

  • Receive Date 07 October 2023
  • Revise Date 19 October 2024
  • Accept Date 21 October 2024
  • Publish Date 05 November 2025