Flood forecasting based on an artificial neural network scheme
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Flood forecasting based on an artificial neural network scheme Francis Yongwa Dtissibe2 · Ado Adamou Abba Ari1,2 · Chafiq Titouna4 · Ousmane Thiare3 · Abdelhak Mourad Gueroui1 Received: 5 February 2020 / Accepted: 27 July 2020 © Springer Nature B.V. 2020
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. Keywords Flood forecasting · Artificial neural networks · Multilayer perceptron · Machine learning * Ado Adamou Abba Ari [email protected] Francis Yongwa Dtissibe francis.yongwa‑dtissibe@univ‑maroua.cm Chafiq Titouna [email protected] Ousmane Thiare [email protected] Abdelhak Mourad Gueroui [email protected] 1
LI‑PaRAD Lab, Université Paris Saclay, University of Versailles Saint-Quentin-en-Yvelines, 45 Avenue des États‑Unis, 78035 Versailles Cedex, France
2
LaRI Lab, University of Maroua, P.O. Box 814 Maroua, Cameroon
3
LANI Lab, Gaston Berger University of Saint-Louis, P.O. Box 234 Saint‑Louis, Senegal
4
LIPADE Lab, University of Paris, 45 Rue des Saints Pères, 75006 Paris, France
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Natural Hazards
1 Introduction Flood risk remains the most widespread natural hazard today, causing huge loss of lives and materials damages (Falah et al. 2019). The 2018 annual report of the United Nations Office for Disaster Risk Reduction (UNISDR) and the Centre for Research on the Epidemiology of Disasters (CRED) of the University of Louvain, Belgium shows that floods once again constituted the natural disaster with the greatest impact on the largest number of people in 2018 (UNISDR 2018). A total of 35.4 million people were confronted with it. In the International Federation of Red Cross and Red Crescent Societies (IFRC) World Disasters Report of 2018, 40.5% of natural disasters in the past decade were floods (IFRC 2018). Therefore, 734 million people have been affected by these floods, and the estimated cost of damage is US $ 691.386 billion. It is therefore necessa
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