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A self-tuning ANN model for simulation and forecasting of surface flows

Author

Listed:
  • Omid Bozorg-Haddad

    (University of Tehran)

  • Mahboubeh Zarezadeh-Mehrizi

    (Tarbiat Modares University)

  • Mehri Abdi-Dehkordi

    (University of Tehran)

  • Hugo A. Loáiciga

    (University of California)

  • Miguel A. Mariño

    (University of California)

Abstract

Artificial neural networks (ANN) are applicable for and forecasting without the need to calculate complex nonlinear functions. This paper evaluates the effectiveness of temperature, evapotranspiration, precipitation and inflow factors, and the lag time of those factors, as variables for simulating and forecasting of runoff. The genetic algorithm (GA) is coupled with ANN to determine the optimal set of variables for streamflow forecasting. The minimization of the total mean square error (MSE) is considered as the objective function of the ANN-GA method in this paper. Our results show the effectiveness of the ANN-GA for simulating and forecasting runoff with consistent accuracy compared with using pure ANN for runoff simulation and forecasting.

Suggested Citation

  • Omid Bozorg-Haddad & Mahboubeh Zarezadeh-Mehrizi & Mehri Abdi-Dehkordi & Hugo A. Loáiciga & Miguel A. Mariño, 2016. "A self-tuning ANN model for simulation and forecasting of surface flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 2907-2929, July.
    • Handle: RePEc:spr:waterr:v:30:y:2016:i:9:d:10.1007_s11269-016-1301-2
    DOI: 10.1007/s11269-016-1301-2
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    References listed on IDEAS

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    1. Omid Haddad & Abbas Afshar & Miguel Mariño, 2006. "Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(5), pages 661-680, October.
    2. Y. Bolouri-Yazdeli & O. Bozorg Haddad & E. Fallah-Mehdipour & M. Mariño, 2014. "Evaluation of Real-Time Operation Rules in Reservoir Systems Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 715-729, February.
    3. Ashkan Shokri & Omid Bozorg Haddad & Miguel Mariño, 2014. "Multi-Objective Quantity–Quality Reservoir Operation in Sudden Pollution," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 567-586, January.
    4. M. Ahmadi & Omid Bozorg Haddad & M. Mariño, 2014. "Extraction of Flexible Multi-Objective Real-Time Reservoir Operation Rules," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(1), pages 131-147, January.
    5. S. Seifollahi-Aghmiuni & O. Bozorg Haddad & M. Mariño, 2013. "Water Distribution Network Risk Analysis Under Simultaneous Consumption and Roughness Uncertainties," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2595-2610, May.
    6. M. Jalali & A. Afshar & M. Mariño, 2007. "Multi-Colony Ant Algorithm for Continuous Multi-Reservoir Operation Optimization Problem," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(9), pages 1429-1447, September.
    7. O. Haddad & M. Tabari & E. Fallah-Mehdipour & M. Mariño, 2013. "Groundwater Model Calibration by Meta-Heuristic Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2515-2529, May.
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    Cited by:

    1. Amir Hamzeh Haghiabi, 2017. "Modeling River Mixing Mechanism Using Data Driven Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(3), pages 811-824, February.
    2. Omid Bozorg-Haddad & Pouria Yari & Mohammad Delpasand & Xuefeng Chu, 2022. "Reservoir operation under influence of the joint uncertainty of inflow and evaporation," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 2914-2940, February.
    3. Wen-Ping Tsai & Yen-Ming Chiang & Jun-Lin Huang & Fi-John Chang, 2016. "Exploring the Mechanism of Surface and Ground Water through Data-Driven Techniques with Sensitivity Analysis for Water Resources Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4789-4806, October.
    4. Mehdi Kazemi & Omid Bozorg-Haddad & Elahe Fallah-Mehdipour & Xuefeng Chu, 2022. "Optimal water resources allocation in transboundary river basins according to hydropolitical consideration," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(1), pages 1188-1206, January.
    5. Ali Ahani & Mojtaba Shourian & Peiman Rahimi Rad, 2018. "Performance Assessment of the Linear, Nonlinear and Nonparametric Data Driven Models in River Flow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(2), pages 383-399, January.
    6. Shunqing Jia & Xihua Wang & Y. Jun Xu & Zejun Liu & Boyang Mao, 2024. "A New Data-Driven Model to Predict Monthly Runoff at Watershed Scale: Insights from Deep Learning Method Applied in Data-Driven Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(13), pages 5179-5194, October.

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