International audienceANR FLASH project (2009-2013) intends to capitalize on the advantages of machine learning methods in order to provide tools for real-time flash floods forecasting. In a first step, water level forecasts were provided based on rain estimation of rainfalls, leading to the design of a demonstrating software. In a second step, weather RADAR measurements will be taken in advantage, as for rainfall estimation than for directly inputs reflectivity to the model. Comparison between the 3-type of inputs (rain gauge rainfall, RADAR rainfall, COMEPHORE reanalysis) will be assessed
The parsimonious time series models used within the Data-Based Mechanistic (DBM) modelling framework...
Machine learning technologies have helped provide answers for problems with a high degree of complex...
Machine learning technologies have helped provide answers for problems with a high degree of complex...
The Liane River is a small costal river, famous for its floods, which can affect the city of Boulogn...
Abstract. The feasibility of flash flood forecasting without making use of rainfall predictions is i...
Floods are quite possibly of the most harming regular disappointment, which can be perceptibly mind ...
International audienceFlash floods are among the deadliest natural hazards. With the increase of wor...
[[abstract]]Nowadays, the degree and scale of flood hazards has been massively increasing as a resul...
Floods are among the most destructive natural disasters, which are highly complex to model. The rese...
The use of machine learning (ML) for predicting high river flow events is gaining prominence and amo...
Flash floods and hurricanes are caused by the release of energy inside the oceans. Hurricanes are v...
International audienceNowadays, floods have become the widest global environmental and economic haza...
Floods are among the most destructive natural disasters, which are highly complex to model. The rese...
Predicting the maximum possible level of water in junctions found in the urban drainage model can be...
This paper describes the development of a back-propagation Neural Network model for predicting flood...
The parsimonious time series models used within the Data-Based Mechanistic (DBM) modelling framework...
Machine learning technologies have helped provide answers for problems with a high degree of complex...
Machine learning technologies have helped provide answers for problems with a high degree of complex...
The Liane River is a small costal river, famous for its floods, which can affect the city of Boulogn...
Abstract. The feasibility of flash flood forecasting without making use of rainfall predictions is i...
Floods are quite possibly of the most harming regular disappointment, which can be perceptibly mind ...
International audienceFlash floods are among the deadliest natural hazards. With the increase of wor...
[[abstract]]Nowadays, the degree and scale of flood hazards has been massively increasing as a resul...
Floods are among the most destructive natural disasters, which are highly complex to model. The rese...
The use of machine learning (ML) for predicting high river flow events is gaining prominence and amo...
Flash floods and hurricanes are caused by the release of energy inside the oceans. Hurricanes are v...
International audienceNowadays, floods have become the widest global environmental and economic haza...
Floods are among the most destructive natural disasters, which are highly complex to model. The rese...
Predicting the maximum possible level of water in junctions found in the urban drainage model can be...
This paper describes the development of a back-propagation Neural Network model for predicting flood...
The parsimonious time series models used within the Data-Based Mechanistic (DBM) modelling framework...
Machine learning technologies have helped provide answers for problems with a high degree of complex...
Machine learning technologies have helped provide answers for problems with a high degree of complex...