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Flood forecasting based on an artificial neural network scheme
- Source :
- Natural Hazards, Natural Hazards, Springer Verlag, 2020, 104 (2), pp.1211-1237. ⟨10.1007/s11069-020-04211-5⟩
- Publication Year :
- 2020
- Publisher :
- HAL CCSD, 2020.
-
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.
- 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
Subjects
Details
- Language :
- English
- ISSN :
- 0921030X and 15730840
- Database :
- OpenAIRE
- Journal :
- Natural Hazards, Natural Hazards, Springer Verlag, 2020, 104 (2), pp.1211-1237. ⟨10.1007/s11069-020-04211-5⟩
- Accession number :
- edsair.doi.dedup.....443e8e05de8655d96b155a5ebc51ec59