The European Forest Fire Information System (EFFIS) has been established by the Joint Research Centre (JRC) and the Directorate General for Environment (DG ENV) of the European Commission (EC) in close collaboration with the Member States and neighbour countries. EFFIS is intended as complementary system to national and regional systems in the countries, providing harmonised information required for international collaboration on forest fire prevention and fighting and in cases of trans-boundary fire events. However, one missing component in the system is a wildfire behaviour model able to cover the whole Europe. We propose a new general conceptualisation for wildfire prediction. It relies on an array-based and semantically enhanced (Semantic Array Programming) application of the Dynamic Data Driven Application Systems (DDDAS) concept, so as to predict spread of large fires at European level. The proposed mathematical framework is designed to simulate with an ensemble strategy the wildfire dynamics under given sequences of actions for controlling the fire spread and updated data- driven information. First results on data and software uncertainties associated with the problem have been presented with a real case study in Spain.
A Data-driven Model for Large Wildfire Behaviour Prediction in Europe
DE RIGO, DANIELE;
2013-01-01
Abstract
The European Forest Fire Information System (EFFIS) has been established by the Joint Research Centre (JRC) and the Directorate General for Environment (DG ENV) of the European Commission (EC) in close collaboration with the Member States and neighbour countries. EFFIS is intended as complementary system to national and regional systems in the countries, providing harmonised information required for international collaboration on forest fire prevention and fighting and in cases of trans-boundary fire events. However, one missing component in the system is a wildfire behaviour model able to cover the whole Europe. We propose a new general conceptualisation for wildfire prediction. It relies on an array-based and semantically enhanced (Semantic Array Programming) application of the Dynamic Data Driven Application Systems (DDDAS) concept, so as to predict spread of large fires at European level. The proposed mathematical framework is designed to simulate with an ensemble strategy the wildfire dynamics under given sequences of actions for controlling the fire spread and updated data- driven information. First results on data and software uncertainties associated with the problem have been presented with a real case study in Spain.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
