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

Author

Listed:
  • Hai Tao

    (Baoji University of Arts and Sciences)

  • Behrooz Keshtegar

    (University of Zabol)

  • Zaher Mundher Yaseen

    (Ton Duc Thang University)

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.

Suggested Citation

  • Hai Tao & Behrooz Keshtegar & Zaher Mundher Yaseen, 2019. "The Feasibility of Integrative Radial Basis M5Tree Predictive Model for River Suspended Sediment Load Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4471-4490, October.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:13:d:10.1007_s11269-019-02378-6
    DOI: 10.1007/s11269-019-02378-6
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    References listed on IDEAS

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    Cited by:

    1. Khabat Khosravi & Ali Golkarian & John P. Tiefenbacher, 2022. "Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 699-716, January.
    2. Elham Ghanbari-Adivi & Mohammad Ehteram & Alireza Farrokhi & Zohreh Sheikh Khozani, 2022. "Combining Radial Basis Function Neural Network Models and Inclusive Multiple Models for Predicting Suspended Sediment Loads," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4313-4342, September.
    3. Bibhuti Bhusan Sahoo & Sovan Sankalp & Ozgur Kisi, 2023. "A Novel Smoothing-Based Deep Learning Time-Series Approach for Daily Suspended Sediment Load Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4271-4292, September.
    4. Khabat Khosravi & Zohreh Sheikh Khozani & Javad Hatamiafkoueieh, 2023. "Prediction of embankments dam break peak outflow: a comparison between empirical equations and ensemble-based machine learning algorithms," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(3), pages 1989-2018, September.

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    More about this item

    Keywords

    Sediment transport modeling; Discharge information; River engineering sustainability; M5 tree model; Hybrid model;
    All these keywords.

    JEL classification:

    • M5 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics

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