Abstract
The term flash-flood refers to the sudden raise in the water levels in a basin due to an abrupt change in the weather conditions. Early detection of flash-floods reduces the harm that they can produce in the infrastructure or even in preventing human losses. Up to now, the studies focus on the dynamics of the basins, determining how the water levels would be in a considered scenario. However, nothing have been done concerning the online prediction of flash-floods. This research focuses on this topic, proposing a Case-Based Reasoning tool to cope with the estimation of the water levels on a basin based on the current basin conditions and the weather forecast. Furthermore, this CBR tool has been designed to work in different basins provided enough data is available, either from past experiences or from simulation. This research is being designed, developed on two real basins, one from Spain and one from France; however, the experimentation has only been addressed with realistic data from the Venero-Claro basin in Spain. Expectancy is that the performance of the CBR tool will perfectly mimic the decision making of the public safety experts.
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Acknowledgements
This research has been founded by European Union’s Horizon 2020 research and innovation programme (project DIH4CPS) under the Grant Agreement no 872548. Furthermore, this research has been funded by the SUDOE Interreg Program -grant INUNDATIO SOE3/P4/E0929-, by the Spanish Ministry of Economics and Industry, grant PID2020-112726RB-I00, by the Spanish Research Agency (AEI, Spain) under grant agreement RED2018-102312-T (IA-Biomed), by CDTI (Centro para el Desarrollo Tecnológico Industrial) under projects CER-20211003 and CER-20211022, by and Missions Science and Innovation project MIG-20211008 (INMERBOT). Also, by Principado de Asturias, grant SV-PA-21-AYUD/2021/50994 and by ICE (Junta de Castilla y León) under project CCTT3/20/BU/0002.
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Fernádez, E., Villar, J.R., Navarro, A., Sedano, J. (2023). Case-Based Reasoning for the Prediction of Flash Flood. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_58
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