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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
Article
  • 39 Downloads

Abstract

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.

Keywords

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

Nomenclature

St

Suspended sediment load

RM5Tree

M5 model tree

RSM

Response surface method

ANN

Artificial neural network

ML

Machine learning

SVM

Support vector machine

ANFIS

Adaptive neuro fuzzy inference system

GP

Genetic programming

GEP

Gene expression programming

MT

Model tree

RT

Regression tree

CART

Classification and regression tree

Q

River flow discharge

Xmin

Minimum discharge

Xmax

Maximum discharge

Xmean

Mean discharge

STD

Standard deviation

MLPNN

Multilayer perceptron neural network

SDR

Standard deviation reduction

PDF

Probability distribution function

CDF

Cumulative destitution function

RMSE

Root mean square error

MAE

Mean absolute error

d

Agreement index

NSE

Nash-Sutcliffe efficiency

W-GEP

wavelet-GEP

NF

Neuro-fuzzy

MI

Mutual information

M

Number of hidden neurons

b

Bias

w

Connection weight

x

Input variable

Open image in new window

Predicted sediment

P(X)

Polynomial basis function

Cj

The center point

erf

Error function

Zi

Standard input variable

N

Number of data point

Notes

Acknowledgements

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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