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Short to Long-Term Forecasting of River Flows by Heuristic Optimization Algorithms Hybridized with ANFIS

Author

Listed:
  • Hossien Riahi-Madvar

    (Vali-e-Asr University of Rafsanjan)

  • Majid Dehghani

    (Vali-e-Asr University of Rafsanjan)

  • Rasoul Memarzadeh

    (Vali-e-Asr University of Rafsanjan)

  • Bahram Gharabaghi

    (University of Guelph)

Abstract

Accurate forecast of short-term to long-term streamflow prediction is of great importance for water resources management. However, with the advent of novel hybrid machine learning methods, it remains unclear whether these hybrid models can outperform the traditional streamflow forecast models. Therefore, in this study, we trained and tested the performance of several evolutionary algorithms, including Fire-Fly Algorithm(FFA), Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Differential Evolution (DE) hybridized with ANFIS. Three forecast horizons, short-term (Daily), mid-term (Weekly and Monthly) and long-term (Annual) with fifteen input-output combinations, a total of 90 models, were developed and tested. A Monte Carlo Simulation (MCS) framework is used for uncertainty analysis. Daily inflow to the Karun III dam, located in the southeast of Iran, for the period of June 2005 to December 2016 were used. Results indicated that: 1) All developed hybrid algorithms significantly outperformed the traditional ANFIS model performance for all prediction horizons. The best hybrid models were ANFIS-GWO1, ANFIS-GWO7 and ANFIS-GWO11 such that the values of R2, RMSE, NSE, and RAE were improved by 12%, 10%, 18.5% and 14.3% for the short-term forecasts, 15%, 13%, 20% and 21.1% for the mid-term forecasts, and 10.3%, 7.5%, 10.5% and 14% for the long-term forecasts; 2) Uncertainty analysis indicates that nearly all hybrid models have significantly reduced uncertainty levels compared to the traditional ANFIS model; and 3) A simple explicit equation based on the hybrid ANFIS results was provided for streamflow forecasting, which is a major advantage compared to the classical blackbox machine learning models.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:4:d:10.1007_s11269-020-02756-5
    DOI: 10.1007/s11269-020-02756-5
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    References listed on IDEAS

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    1. Parisa Noorbeh & Abbas Roozbahani & Hamid Kardan Moghaddam, 2020. "Annual and Monthly Dam Inflow Prediction Using Bayesian Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2933-2951, July.
    2. Hossein Bonakdari & Isa Ebtehaj & Pijush Samui & Bahram Gharabaghi, 2019. "Lake Water-Level fluctuations forecasting using Minimax Probability Machine Regression, Relevance Vector Machine, Gaussian Process Regression, and Extreme Learning Machine," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3965-3984, September.
    3. 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.
    4. Ahmad Khazaee Poul & Mojtaba Shourian & Hadi Ebrahimi, 2019. "A Comparative Study of MLR, KNN, ANN and ANFIS Models with Wavelet Transform in Monthly Stream Flow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2907-2923, June.
    5. Zaher Mundher Yaseen & Sujay Raghavendra Naganna & Zulfaqar Sa’adi & Pijush Samui & Mohammad Ali Ghorbani & Sinan Q. Salih & Shamsuddin Shahid, 2020. "Hourly River Flow Forecasting: Application of Emotional Neural Network Versus Multiple Machine Learning Paradigms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1075-1091, February.
    6. 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.
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    2. Ihab K. A. Hamdan & Wulamu Aziguli & Dezheng Zhang & Eli Sumarliah, 2023. "Machine learning in supply chain: prediction of real-time e-order arrivals using ANFIS," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 549-568, March.
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    4. Maha Shabbir & Sohail Chand & Farhat Iqbal, 2022. "A Novel Hybrid Method for River Discharge Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 253-272, January.

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