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Environmental Science and Pollution Research

, Volume 25, Issue 35, pp 35693–35706 | Cite as

Modeling daily suspended sediment load using improved support vector machine model and genetic algorithm

  • Mitra Rahgoshay
  • Sadat Feiznia
  • Mehran Arian
  • Seyed Ali Asghar Hashemi
Research Article
  • 35 Downloads

Abstract

Prediction of sediment volume and sediment load is always one of the important issues for decision-makers of watershed basins. The present study investigated the daily suspended sediment load in a watershed basin using the improved support vector machine method. Since in most of the previous studies, the coefficients of the support vector machine method had been calculated based on trial and error, in the present study, the combination of the support vector machine and the genetic algorithm is used. In the first step, the unknown parameters of the support vector machine are calculated and then, the sediment load simulation is performed. Two case studies in the present work involve two earth dams in Semnan Province called Veynakeh and Royan. Furthermore, multivariate adaptive regression spline (MARS) and MT tree model (M5T) methods are used for comparison. The results indicated that the input combination of discharge data at the current time and one, two, and three previous days has the best performance for all models. Also, the support vector machine-genetic algorithm (SVM-GA) model has a lower root mean square error (RMSE) and mean absolute error (MAE) compared to the MARS and M5T models for both stations. In addition, comparing observational data with simulation data based on the R2 coefficient suggested that the SVM-GA model offers more accurate results than the other two methods. Accordingly, the SVM-GA method used in this study has a high potential for simulating sediment volume.

Keywords

Sediment Genetic algorithm Support vector machine Tree model 

Notes

Author contribution

A new method for the support vector machine was used to simulate the sediment load. All the authors contributed to the manuscript development by writing, discussing changes for clarity, and technical usefulness and correction.

Supplementary material

11356_2018_3533_MOESM1_ESM.docx (14 kb)
ESM 1 (DOCX 14 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Mitra Rahgoshay
    • 1
  • Sadat Feiznia
    • 2
  • Mehran Arian
    • 3
  • Seyed Ali Asghar Hashemi
    • 4
  1. 1.Department of Earth Sciences, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Faculty of Natural ResourcesUniversity of TehranKarajIran
  3. 3.Department of Earth Sciences, Science and Research BranchIslamic Azad UniversityTehranIran
  4. 4.Department of Watershed ManagementAgricultural and Natural Resources Research and Education CenterSemnanIran

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