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Artificial Neural Network and Support Vector Machine Models for Inflow Prediction of Dam Reservoir (Case Study: Zayandehroud Dam Reservoir)

Author

Listed:
  • Mohammad Babaei

    (University of Isfahan)

  • Ramtin Moeini

    (University of Isfahan)

  • Eghbal Ehsanzadeh

    (Ilam University)

Abstract

Inflow prediction of reservoirs is of considerable importance due to its application in water resources management related to downstream water release planning and flood protection. Therefore, in this research, different new input patterns for predicting inflow to Zayandehroud dam reservoir is proposed employing artificial neural network (ANN) and support vector machine (SVM) models. Nine different models with different patterns of input data such as inflow to the dam reservoir considering time duration lags, time index, and monthly rainfall of Ghaleh-Shahrokh station have been proposed to predict the inflow to the dam reservoir. Comparison of the results indicates that the ninth proposed model has the least error for inflow prediction in which the results of SVM model outperform those of ANN model. That is, the least error has been obtained using the ninth SVM (ANN) model with correlation coefficient (R) values of 0.8962 (0.89296), 0.9303 (0.92983) and 0.9622 (0.95333) and root mean squared error (RMSE) values of 47.9346 (48.5441), 42.69093 (43.748) and 23.56193 (28.5125) for training, validation and test data, respectively.

Suggested Citation

  • Mohammad Babaei & Ramtin Moeini & Eghbal Ehsanzadeh, 2019. "Artificial Neural Network and Support Vector Machine Models for Inflow Prediction of Dam Reservoir (Case Study: Zayandehroud Dam Reservoir)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 2203-2218, April.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:6:d:10.1007_s11269-019-02252-5
    DOI: 10.1007/s11269-019-02252-5
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    References listed on IDEAS

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    1. Lima, L.M. Marangon & Popova, E. & Damien, P., 2014. "Modeling and forecasting of Brazilian reservoir inflows via dynamic linear models," International Journal of Forecasting, Elsevier, vol. 30(3), pages 464-476.
    2. Taymoor Awchi, 2014. "River Discharges Forecasting In Northern Iraq Using Different ANN Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 801-814, February.
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    Cited by:

    1. Sarmad Dashti Latif & Ali Najah Ahmed & Edlic Sathiamurthy & Yuk Feng Huang & Ahmed El-Shafie, 2021. "Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia," 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. 109(1), pages 351-369, October.
    2. Hossien Riahi-Madvar & Majid Dehghani & Rasoul Memarzadeh & Bahram Gharabaghi, 2021. "Short to Long-Term Forecasting of River Flows by Heuristic Optimization Algorithms Hybridized with ANFIS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(4), pages 1149-1166, March.
    3. S. Khorram & N. Jehbez, 2023. "A Hybrid CNN-LSTM Approach for Monthly Reservoir Inflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 4097-4121, August.

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