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A Comparative Assessment of Artificial Neural Network, Generalized Regression Neural Network, Least-Square Support Vector Regression, and K-Nearest Neighbor Regression for Monthly Streamflow Forecasting in Linear and Nonlinear Conditions

Author

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  • Fereshteh Modaresi

    (University of Tehran)

  • Shahab Araghinejad

    (University of Tehran)

  • Kumars Ebrahimi

    (University of Tehran)

Abstract

Monthly forecasting of streamflow is of particular importance in water resources management especially in the provision of rule curves for dams. In this paper, the performance of four data-driven models with different structures including Artificial Neural Network (ANN), Generalized Regression Neural Network (GRNN), Least Square-Support Vector Regression (LS-SVR), and K-Nearest Neighbor Regression (KNN) are evaluated in order to forecast monthly inflow to Karkheh dam, Iran, in linear and non-linear conditions while the optimized values of the model parameters are determined in the same condition via the Leave-One-Out Cross Validation (LOOCV) method. Results show that the performance of the models is different in linear and nonlinear conditions; the cumulative ranking of the models according to the three assessment criteria including NSE, RMSE and R2 indicates that ANN performs best in linear conditions while LS-SVR, GRNN and KNN are in the next ranks, respectively. But in nonlinear conditions, the best performance belongs to LS-SVR, followed by KNN, ANN, and GRNN models.

Suggested Citation

  • Fereshteh Modaresi & Shahab Araghinejad & Kumars Ebrahimi, 2018. "A Comparative Assessment of Artificial Neural Network, Generalized Regression Neural Network, Least-Square Support Vector Regression, and K-Nearest Neighbor Regression for Monthly Streamflow Forecasti," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 243-258, January.
    • Handle: RePEc:spr:waterr:v:32:y:2018:i:1:d:10.1007_s11269-017-1807-2
    DOI: 10.1007/s11269-017-1807-2
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    References listed on IDEAS

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    1. Fereshteh Modaresi & Shahab Araghinejad, 2014. "A Comparative Assessment of Support Vector Machines, Probabilistic Neural Networks, and K-Nearest Neighbor Algorithms for Water Quality Classification," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4095-4111, September.
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    3. Rana Muhammad Adnan Ikram & Leonardo Goliatt & Ozgur Kisi & Slavisa Trajkovic & Shamsuddin Shahid, 2022. "Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction," Mathematics, MDPI, vol. 10(16), pages 1-30, August.
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    10. Pin-Chun Huang & Kuo-Lin Hsu & Kwan Tun Lee, 2021. "Improvement of Two-Dimensional Flow-Depth Prediction Based on Neural Network Models By Preprocessing Hydrological and Geomorphological Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 1079-1100, February.
    11. Wenxin Xu & Jie Chen & Xunchang J. Zhang, 2022. "Scale Effects of the Monthly Streamflow Prediction Using a State-of-the-art Deep Learning Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3609-3625, August.
    12. Mostaghimzadeh, Ehsan & Adib, Arash & Ashrafi, Seyed Mohammad & Kisi, Ozgur, 2022. "Investigation of a composite two-phase hedging rule policy for a multi reservoir system using streamflow forecast," Agricultural Water Management, Elsevier, vol. 265(C).
    13. Mohammad S. Islam & Shahid Husain & Jawed Mustafa & Yuantong Gu, 2022. "A Novel Machine Learning Prediction Model for Aerosol Transport in Upper 17-Generations of the Human Respiratory Tract," Future Internet, MDPI, vol. 14(9), pages 1-16, August.
    14. Marta Matyjaszek & Gregorio Fidalgo Valverde & Alicja Krzemień & Krzysztof Wodarski & Pedro Riesgo Fernández, 2020. "Optimizing Predictor Variables in Artificial Neural Networks When Forecasting Raw Material Prices for Energy Production," Energies, MDPI, vol. 13(8), pages 1-15, April.

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