Journal of Range and Watershed Managment

Comparison the efficiency of M5 and Genetic Programming on Suspended Sediment Load Estimation of Rivers

Document Type : Research Paper

Authors

javad zahiri 1
hadis shahrokhi 2
Ahmad Jafari 3

1 Assistant Professor, Khuzestan Ramin Agriculture and Natural Resources University

2 Graduate from Ramin Agricultural and Natural Resources University

3 Assistant Professor, Department of Water Engineering, Khuzestan Ramin Agriculture and Natural Resources University.

https://doi.org/10.22059/jrwm.2018.232883.1121
Abstract
Suspended sediment load estimation is one of the most important and complicated debates on sediment transport and river engineering. Therefore, the variety of methods developed to estimate suspended sediment load in rivers. According to the empirical nature of these methods, the results have low accuracy and vary so widely from one method to another. Recently, with advances in computer science, various algorithms such as tree based methods have been developed. In this study, sediment rating curve method along with new algorithms such as M5 and Genetic Programing (GP) are used for estimating suspended sediment load in rivers. Flow and sediment discharge data at five hydrometric stations, Behbahan and Cham Nezam on Maroon River, Jow Kanak on Allah River and Moshrageh and Shadegan on Jarahi River are used in present study. The efficiency of tree models, in all stations, was greater than sediment rating curve method, and the RMSE performance of M5 method is 7 to 41 percent superior to sediment rating curve method. The results of this study indicate the close proximity of both M5 and GP efficiency, which according to the simple and conceptual structure of M5, this method is proposed to estimate suspended sediment load in river streams.

Keywords

Suspended sediment load
Tree algorithms
Sediment Rating Curve
M5 algorithm
Genetic Programing
Journal of Range and Watershed Managment
Volume 71, Issue 1
Spring 2018
Pages 173-187

  • Receive Date 08 May 2017
  • Revise Date 24 May 2018
  • Accept Date 12 January 2018
  • Publish Date 22 May 2018

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