Data injection at execution time in grid environments using dynamic data driven application system for wildland fire spread prediction

In our research work, we use two Dynamic Data Driven Application System (DDDAS) methodologies to predict wildfire propagation. Our goal is to build a system that dynamically adapts to constant changes in environmental conditions when a hazard occurs and under strict real-time deadlines. For this purpose, we are on the way of building a parallel wildfire prediction method, which is able to assimilate real-time data to be injected in the prediction process at execution time. In this paper, we propose a strategy for data injection indistributed environments.