A High Performance Computing Framework for Continental-Scale Forest Fire Spread Prediction

Abstract

Many scientific works have focused on developing propagation models that predict forest fire behavior. These models require a precise knowledge of the environment where the fire is taking place. Geographical Information Systems allow us determining and building the different information layers that define the terrain and the fire. These data, along with meteorological information from weather services, enables the simulation based on real conditions. However, fire spread prediction models require a set of input parameters that, in some cases, are difficult to know or even estimate precisely. Therefore, a framework, based on a genetic algorithm calibration stage, was introduced to reduce the uncertainty in the input parameters and improve the accuracy of the predictions. This stage is implemented using a MPI master/worker scheme and an OpenMP parallel version of the fire spread simulator. Additionally, the whole system is run using suitable automatic worker-assignment and core-allocation policies to respect the existing time restrictions, inherent to this real-world problem. This paper details the process of obtaining the necessary input data as well as the parallel evolutionary framework that delivers the final prediction. A real case study is presented to illustrate the way this framework works.