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Flood forecasting based on an artificial neural network scheme

Author

Listed:
  • Francis Yongwa Dtissibe

    (University of Maroua)

  • Ado Adamou Abba Ari

    (University of Versailles Saint-Quentin-en-Yvelines
    University of Maroua)

  • Chafiq Titouna

    (University of Paris)

  • Ousmane Thiare

    (Gaston Berger University of Saint-Louis)

  • Abdelhak Mourad Gueroui

    (University of Versailles Saint-Quentin-en-Yvelines)

Abstract

Nowadays, floods have become the widest global environmental and economic hazard in many countries, causing huge loss of lives and materials damages. It is, therefore, necessary to build an efficient flood forecasting system. The physical-based flood forecasting methods have indeed proven to be limited and ineffective. In most cases, they are only applicable under certain conditions. Indeed, some methods do not take into account all the parameters involved in the flood modeling, and these parameters can vary along a channel, which results in obtaining forecasted discharges very different from observed discharges. While using machine learning tools, especially artificial neural networks schemes appears to be an alternative. However, the performance of forecasting models, as well as a minimum error of prediction, is very interesting and challenging issues. In this paper, we used the multilayer perceptron in order to design a flood forecasting model and used discharge as input–output variables. The designed model has been tested upon intensive experiments and the results showed the effectiveness of our proposal with a good forecasting capacity.

Suggested Citation

  • Francis Yongwa Dtissibe & Ado Adamou Abba Ari & Chafiq Titouna & Ousmane Thiare & Abdelhak Mourad Gueroui, 2020. "Flood forecasting based on an artificial neural network scheme," 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. 104(2), pages 1211-1237, November.
  • Handle: RePEc:spr:nathaz:v:104:y:2020:i:2:d:10.1007_s11069-020-04211-5
    DOI: 10.1007/s11069-020-04211-5
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    Citations

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    Cited by:

    1. Marzieh Khajehali & Hamid R. Safavi & Mohammad Reza Nikoo & Mahmood Fooladi, 2024. "A fusion-based framework for daily flood forecasting in multiple-step-ahead and near-future under climate change scenarios: a case study of the Kan River, Iran," 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. 120(9), pages 8483-8504, July.
    2. Sarmad Dashti Latif & Ali Najah Ahmed, 2023. "A review of deep learning and machine learning techniques for hydrological inflow forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 12189-12216, November.

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