The use of computational intelligence base models in suspended sediment load estimation (Case study: Gillan province)

Document Type : Research Paper

Authors

1 M.Sc. Student of watershed management/Ardakan University

2 Academic Staff / Ardakan University, Faculty of Agr. and Natural Resources

Abstract

Understanding of suspended sediment rate is one of the fundamental problems in water projects which water engineers consistently have involved with it. Wrong estimations in sediment transport cause incorrect design and destruction of hydraulic systems. Due to the difficulty of suspended sediment measurements, sediment rating curves is considered as the most common method for estimating the suspended sediment load. The main purpose of this research is the capability challenge of this method in comparison to some state of the art models. In this study, we selected some computational intelligence models (i.e. K-nearest neighbor (KNN), artificial neural networks (ANN), Gaussian processes (GP), decision trees of M5, support vector machine (SVM) and evolutionary support vector machine (ESVM)) and compared them with their sediment rating model in 8 basins located in Gilan province. Daily sediment and discharge data considered as the input data for 30-years. Evaluation of the results indicated that the Gaussian process model has the lowest residual sum of squares (RMSE) and the highest correlation coefficient (r) than the other models.

Keywords


Volume 71, Issue 1
June 2018
Pages 45-60
  • Receive Date: 17 December 2016
  • Revise Date: 25 May 2018
  • Accept Date: 07 April 2018
  • First Publish Date: 22 May 2018