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Forest Fire Simulations

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Applying Grid and urgent computing solutions to forest fire propagation prediction

A forest fire, as other natural hazards, is a quite significant problem that every year causes significant damages around the world. This kind of hazard provokes significant losses from the ecological, economical, social and human point of view. Therefore, a quick response when an emergency occurs is crucial to minimize its effects. In this context, the accurate prediction of the propagation of forest fire is critical to use the available resources to fight against the fire in the most efficient way. Several models have been developed by researchers from different fields to represent and predict the fire propagation. These models require input parameters including terrain topography, vegetation conditions and meteorological variables to produce precise and accurate predictions. Some of these parameters are uniform and static, but others have a spatial distribution and a temporal variation, and are difficult to know precisely their values beforehand. So, a two-stage prediction methodology was developed. This methodology calibrates the parameters by applying artificial intelligence evolutionary techniques. In the calibration stage the actual propagation of the fire is observed and then the input parameters are calibrated. The values that best reproduce the actual propagation of the fire are used in the prediction stage. A developed two-stage prediction methodology is independent from the propagation model and simulation kernel considered. The model itself appears as a black-box and the only point that must be considered are the input parameters required for each simulator. UAB used FireSim and FarSite, and also have some experience with FireStation. Currently, the UAB (Universitat Autonoma de Barcelona, Spain) team is working on the coupling of meteorological prediction models, such as WRF, and wind field models, such as Wind Ninja. These models become a key issue since the wind is a key factor on the prediction quality. However, such models are even more time consuming that the fire propagation model itself.

An OGC-WS Framework to Run FireStation on the Grid

The CROSS-Fire project aims to develop a grid-based framework as a risk management decision support system for the civil protection authorities, using forest fires as the main case study and FireStation as the standalone CAD application that simulates the fire spread over complex topography. The CROSS-Fire initial tasks have been focussed on the development of a parallel version of the fire simulator engine (P-FireStation), and its porting into the EGEE grid environment (G-FireStation). The main contribution of this communication lies on the definition of an OGC-WS framework to enable a basic set of standard geospatial services that will allow FireStation to be interoperable with standard-based Spatial Data Infrastructures. The P-FireStation version explores the inherent parallel environment offered by clusters at each site of the EGEE grid, to support larger data sets and to improve the accuracy of the predictions. This parallel version relies on the MPI protocol and supports larger data sets taking advantage of the MPI parallel I/O facilities. The G-FireStation version integrates EGEE grid facilities, namely the gLite data management services and tools to access data, the AMGA gLite grid metadata catalogue to manage the simulation I/O data, and the WatchDog tool to monitor and provide data for the interactive control of simulations. The approach will make G-FireStation more interoperable, allowing to access different data providers and publishing output data for further processing, following the guidelines of the EC CYCLOPS project.