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Optimal Design and Feature Selection by Genetic Algorithm for Emotional Artificial Neural Network (EANN) in Rainfall-Runoff Modeling

Author

Listed:
  • Amir Molajou

    (Iran University of Science & Technology)

  • Vahid Nourani

    (University of Tabriz
    Near East University)

  • Abbas Afshar

    (Iran University of Science & Technology)

  • Mina Khosravi

    (Iran University of Science & Technology)

  • Adam Brysiewicz

    (Institute of Technology and Life Sciences)

Abstract

Rainfall-runoff (r-r) modeling at different time scales is considered as a significant issue in hydro-environmental planning. As a first hydrological implementation, for one-time-ahead r-r modeling of two watersheds with totally distinct climatic conditions, Genetic Algorithm (GA, as a global search technique) and Emotional Artificial Neural Network (EANN, as a new production of Artificial Intelligence (AI) based methods that simulated based on the brain neurophysiological structure) was combined. Determining the optimal architecture of AI-based networks is vital for increasing the accuracy of prediction by the network and also to reduce run-time. In the current study, GA has been implemented to choose the important features candidate as EANN input and automatically diagnose the optimal number of hidden nodes and hormones simultaneously. The acquired results indicated a better representation of the proposed hybrid GA-EANN model compared to the sole ANN and EANN. Numerical identification of obtained results revealed that the proposed hybrid GA-EANN model might enhance the better results than the EANN model up to 19% and 35% in terms of testing suitability criteria for Aji Chai and Murrumbidgee catchments, respectively.

Suggested Citation

  • Amir Molajou & Vahid Nourani & Abbas Afshar & Mina Khosravi & Adam Brysiewicz, 2021. "Optimal Design and Feature Selection by Genetic Algorithm for Emotional Artificial Neural Network (EANN) in Rainfall-Runoff Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2369-2384, June.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:8:d:10.1007_s11269-021-02818-2
    DOI: 10.1007/s11269-021-02818-2
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    References listed on IDEAS

    as
    1. Rana Muhammad Adnan & Andrea Petroselli & Salim Heddam & Celso Augusto Guimarães Santos & Ozgur Kisi, 2021. "Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach," 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. 105(3), pages 2987-3011, February.
    2. Mohammad Rezaie-Balf & Zahra Zahmatkesh & Sungwon Kim, 2017. "Soft Computing Techniques for Rainfall-Runoff Simulation: Local Non–Parametric Paradigm vs. Model Classification Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3843-3865, September.
    3. 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.
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    Cited by:

    1. Mina Khosravi & Abbas Afshar & Amir Molajou, 2022. "Decision Tree-Based Conditional Operation Rules for Optimal Conjunctive Use of Surface and Groundwater," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 2013-2025, April.
    2. Wongchai Anupong & Muhsin Jaber Jweeg & Sameer Alani & Ibrahim H. Al-Kharsan & Aníbal Alviz-Meza & Yulineth Cárdenas-Escrocia, 2023. "Comparison of Wavelet Artificial Neural Network, Wavelet Support Vector Machine, and Adaptive Neuro-Fuzzy Inference System Methods in Estimating Total Solar Radiation in Iraq," Energies, MDPI, vol. 16(2), pages 1-14, January.
    3. Abbas Afshar & Elham Soleimanian & Hossein Akbari Variani & Masoud Vahabzadeh & Amir Molajou, 2022. "The conceptual framework to determine interrelations and interactions for holistic Water, Energy, and Food Nexus," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(8), pages 10119-10140, August.

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