1. Flood forecasting based on an artificial neural network scheme
- Author
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Chafiq Titouna, Abdelhak Mourad Gueroui, Ousmane Thiare, Ado Adamou Abba Ari, Francis Yongwa Dtissibe, University of Maroua (UMa), Laboratoire d'Informatique Parallélisme Réseaux Algorithmes Distribués (LI-PaRAD), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Laboratoire d'Informatique Paris Descartes (LIPADE (URP_2517)), Université de Paris (UP), Laboratoire d'Analyse Numérique et Informatique [Sénégal] (LANI), Université Gaston Berger de Saint-Louis Sénégal (UGB), and We like to thank the editor and the anonymous reviewers for their valuable remarks that helped us in better improving the content and presentation of the paper.
- Subjects
021110 strategic, defence & security studies ,Atmospheric Science ,Flood forecasting ,010504 meteorology & atmospheric sciences ,Artificial neural network ,Flood myth ,Artificial neural networks ,Computer science ,0211 other engineering and technologies ,02 engineering and technology ,01 natural sciences ,Industrial engineering ,Hazard ,13. Climate action ,Order (exchange) ,Multilayer perceptron ,Natural hazard ,Machine learning ,[SDE]Environmental Sciences ,Earth and Planetary Sciences (miscellaneous) ,0105 earth and related environmental sciences ,Water Science and Technology ,Communication channel - Abstract
International audience; 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.
- Published
- 2020