Efficient Cloud-Based Calibration of Input Data for Forest Fire Spread Prediction

Every year, forest fires cause damages to biodiversity, atmosphere, and economy. To face such permanent threat, wildfire analysts rely on emerging and well established technologies to determine fire behavior and propagation patterns. Nevertheless, input data describing fire scenarios are subject to high levels of uncertainty that represent a serious challenge for the correctness of the prediction. The unknown parameters need to be adjusted, and an input data calibration phase is carried over following a genetic algorithm strategy. The calibrated input is then pipelined into the actual prediction phase. In addition, this two-stage prediction scheme is leveraged by the cloud computing, which enables high level of parallelism on demand, almost real-time elasticity and unlimited scalability. All of them at a low-cost strategy. In this paper, to obtain more accurate prediction results and efficient use of cloud resources in the compute-intensive calibration phase, we propose a new fitness function in tandem with a strict deadline policy that decreases overall processing time. In consonance with the hard-deadline-driven nature of fire extinction activities, the proposed strategies improve the genetic algorithm convergence and decrease the response time for the calibration stage, setting up an important upper bound limit to the critical compute-intensive adjustment phase. For the case study evaluated, only 3.87% of of accuracy loss is given out in exchange of a guarantee that the calibration phase will never last more than 50 minutes in the worst case.