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Combining Radial Basis Function Neural Network Models and Inclusive Multiple Models for Predicting Suspended Sediment Loads

Author

Listed:
  • Elham Ghanbari-Adivi

    (Shahrekord University)

  • Mohammad Ehteram

    (Semnan University)

  • Alireza Farrokhi

    (Alaodoleh Semnani Institute of Higher Education (ASIHE))

  • Zohreh Sheikh Khozani

    (Bauhaus Universität Weimar)

Abstract

An important issue in water engineering is predicting suspended sediment load (SSL). For the Telar River and its tributaries, this study employs an inclusive multiple model (IMM) to predict SSL. Telar River branches into two main branches: Telar and Kasilian. The modeling process consisted of two levels: 1) creating hybrid models and 2) creating ensemble models. At the first level, the Honeybadger optimization algorithm (HBOA), salp swarm algorithm (SSA), and particle swarm optimization (PSO) were applied to set the parameters of the radial basis function neural network (RBFNN) models. The IMM model was used to integrate the outputs of the RBFNN-HBOA, RBFNN-SSA, RBFNN-PSO, and RBFNN models into the RBFNN model at the second level. Inputs to the models included lagged rainfall, discharge, and SSL. Several new ideas have been introduced in the current paper, including hybrid RBFNN models, a gamma test for selecting optimal input combinations, an analysis of output uncertainty, and an advanced IMM for SSL prediction. Various performance evaluation criteria, including root mean square error (RMSE), Nash Sutcliffe Efficiency (NSE), mean absolute error (MAE), and percentage bias (PBIAS), were used to evaluate the models. The comparative results indicated high accuracy of IMM with an MAE of 0.983, NSE of 0.254, PBIAS of 0.991 at Telar station. The training MAE of the IMM model was 4.4%, 4.8%, 6.7%, 52%, and 9.2% lower than that of the RBFNN-HBOA, RBFNN-SSA, RBFNN-PSO, and RBFNN models at Kasilian station. The study results revealed that the IMM and RBFNN-HBOA provided lower uncertainty than the other RBFNN models. Thus, the IMM model represents the most accurate estimation of SSL.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:11:d:10.1007_s11269-022-03256-4
    DOI: 10.1007/s11269-022-03256-4
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    References listed on IDEAS

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    1. Melesse, A.M. & Ahmad, S. & McClain, M.E. & Wang, X. & Lim, Y.H., 2011. "Suspended sediment load prediction of river systems: An artificial neural network approach," Agricultural Water Management, Elsevier, vol. 98(5), pages 855-866, March.
    2. Sarita Gajbhiye Meshram & Vijay P. Singh & Ozgur Kisi & Vahid Karimi & Chandrashekhar Meshram, 2020. "Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4561-4575, December.
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    4. Javad Zahiri & Zeynab Mollaee & Mohammad Reza Ansari, 2020. "Estimation of Suspended Sediment Concentration by M5 Model Tree Based on Hydrological and Moderate Resolution Imaging Spectroradiometer (MODIS) Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(12), pages 3725-3737, September.
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    6. Hamid Moeeni & Hossein Bonakdari, 2018. "Impact of Normalization and Input on ARMAX-ANN Model Performance in 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. 32(3), pages 845-863, February.
    7. Vahid Nourani & Amir Molajou & Ali Davanlou Tajbakhsh & Hessam Najafi, 2019. "A Wavelet Based Data Mining Technique for Suspended Sediment Load Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(5), pages 1769-1784, March.
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    1. 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.
    2. Mahdie Afshari Nia & Fatemeh Panahi & Mohammad Ehteram, 2023. "Convolutional Neural Network- ANN- E (Tanh): A New Deep Learning Model for Predicting Rainfall," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1785-1810, March.
    3. Mohammad Ehteram & Ali Najah Ahmed & Zohreh Sheikh Khozani & Ahmed El-Shafie, 2023. "Convolutional Neural Network -Support Vector Machine Model-Gaussian Process Regression: A New Machine Model for Predicting Monthly and Daily Rainfall," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3631-3655, July.

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