Water Resources Management

, Volume 33, Issue 13, pp 4471–4490 | Cite as

The Feasibility of Integrative Radial Basis M5Tree Predictive Model for River Suspended Sediment Load Simulation

  • Hai Tao
  • Behrooz Keshtegar
  • Zaher Mundher YaseenEmail author


Accurate suspended sediment transport prediction is highly significant for multiple river engineering sustainability. Conceptually evidenced, sediment load transport is highly stochastic, spatial distributed and redundant pattern due to the incorporation of various hydrological and morphological variables such as river flow discharge and sediment physical properties. The motivation of this study is to explore the feasibility of newly intelligent model called Radial basis M5 model tree (RM5Tree) for suspended sediment load (St) prediction for daily scale information at Trenton hydrological station, Delaware River. Numerous input combination attributes are formulated based on the preceding information of sediment and river flow discharge. The prediction accuracy “based statistical and graphical visualizations” of the proposed model validated against numerous well-established predictive models including response surface method (RSM), artificial neural network (ANN) and classical M5Tree based models. The investigated input combinations behaved differently from one case to another. The optimum input combination attributes are included two months lead times of sediment and discharge information to predict one step ahead St. The attained results of the proposed RM5Tree model exhibited a remarkable prediction accuracy with minimal values of root mean square error (RMSE≈2091 ton/day) and coefficient of determination (R2≈0.86). This presenting a percentage of enhancement in the prediction accuracies by (51.6, 53.1 and 26.3) over (RSM, ANN and M5Tree) optimal models over the testing phase.


Sediment transport modeling Discharge information River engineering sustainability M5 tree model Hybrid model 



Suspended sediment load


M5 model tree


Response surface method


Artificial neural network


Machine learning


Support vector machine


Adaptive neuro fuzzy inference system


Genetic programming


Gene expression programming


Model tree


Regression tree


Classification and regression tree


River flow discharge


Minimum discharge


Maximum discharge


Mean discharge


Standard deviation


Multilayer perceptron neural network


Standard deviation reduction


Probability distribution function


Cumulative destitution function


Root mean square error


Mean absolute error


Agreement index


Nash-Sutcliffe efficiency






Mutual information


Number of hidden neurons




Connection weight


Input variable

Open image in new window

Predicted sediment


Polynomial basis function


The center point


Error function


Standard input variable


Number of data point



This work was supported by University of Zabol under Grant No. UOZ-GR-9618-1.

Compliance with Ethical Standards

Conflict of Interest

The authors have no conflict of interest to publish this research.


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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Computer Science DepartmentBaoji University of Arts and SciencesShaanxiChina
  2. 2.Department of Civil EngineeringUniversity of ZabolZabolIran
  3. 3.Sustainable Developments in Civil Engineering Research Group, Faculty of Civil EngineeringTon Duc Thang UniversityHo Chi Minh CityVietnam

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