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Flood-routing modeling with neural network optimized by social-based algorithm

Author

Listed:
  • Mehdi Nikoo

    (Islamic Azad University)

  • Fatemeh Ramezani

    (Islamic Azad University)

  • Marijana Hadzima-Nyarko

    (University of J.J. Strossmayer)

  • Emmanuel Karlo Nyarko

    (University J.J. Strossmayer in Osijek)

  • Mohammad Nikoo

    (Islamic Azad University , Ahvaz Branch)

Abstract

Forecasting and operational routing flood requires accurate forecasts on proper feed time, to be able to issue suitable warnings and take suitable emergency actions. Flood-routing problem is one of the most complicated matters in hydraulics of open channels and river engineering. Flood routing is the process of computing the progressive time and shape of a flood wave at successive points along a river. To get an approximate solution of the flood-routing problem, different techniques are used. This paper describes an approach to train artificial neural network (ANN) using social-based algorithm (SBA). The approach illustrates feed-forward neural network optimization for the flood-routing problem of Kheir Abad River called FF-SBA. To this end, the number and effective time lag of input data in ANN models are initially determined by means of linear correlation between input and output time series; subsequently, the weights of the feed-forward network is optimized by SBA. Optimization algorithms and statistical models like Genetic Algorithm and linear regression are compared to FF-SBA. Compared to the results of optimization algorithms and statistical models, the FF-SBA model for the Kheir Abad River in Iran shows more flexibility and accuracy.

Suggested Citation

  • Mehdi Nikoo & Fatemeh Ramezani & Marijana Hadzima-Nyarko & Emmanuel Karlo Nyarko & Mohammad Nikoo, 2016. "Flood-routing modeling with neural network optimized by social-based algorithm," 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. 82(1), pages 1-24, May.
    • Handle: RePEc:spr:nathaz:v:82:y:2016:i:1:d:10.1007_s11069-016-2176-5
    DOI: 10.1007/s11069-016-2176-5
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    Cited by:

    1. Gordana Pavić & Marijana Hadzima-Nyarko & Borko Bulajić & Željka Jurković, 2020. "Development of Seismic Vulnerability and Exposure Models—A Case Study of Croatia," Sustainability, MDPI, vol. 12(3), pages 1-24, January.
    2. Peyman Yariyan & Saeid Janizadeh & Tran Phong & Huu Duy Nguyen & Romulus Costache & Hiep Le & Binh Thai Pham & Biswajeet Pradhan & John P. Tiefenbacher, 2020. "Improvement of Best First Decision Trees Using Bagging and Dagging Ensembles for Flood Probability Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 3037-3053, July.
    3. Gordana Pavić & Marijana Hadzima-Nyarko & Borko Bulajić, 2020. "A Contribution to a UHS-Based Seismic Risk Assessment in Croatia—A Case Study for the City of Osijek," Sustainability, MDPI, vol. 12(5), pages 1-24, February.
    4. Reyhaneh Akbari & Masoud-Reza Hessami-Kermani & Saeed Shojaee, 2020. "Flood Routing: Improving Outflow Using a New Non-linear Muskingum Model with Four Variable Parameters Coupled with PSO-GA Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3291-3316, August.
    5. Mohammad R. Hassanvand & Hojat Karami & Sayed-Farhad Mousavi, 2018. "Investigation of neural network and fuzzy inference neural network and their optimization using meta-algorithms in river flood routing," 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. 94(3), pages 1057-1080, December.

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