Oriol Rios, M. Miguel Valero, Elsa Pastor and Eulalia Planas

Optimization strategy exploration in a wildfire propagation data driven system


The increasing capacity to gather data of an on-going wildfire operation has triggered the methods and strategies to incorporate these data to a flexible model to improve forecasting accuracy and validity. In the present paper we discuss the optimization strategy included in an inverse model algorithm based on semi-empirical fire spread model fed with infra-red airborne acquired images. The algorithm calibrates 7 parameters and incorporates a topographic diagnosis wind model. The optimization problem is shown to be a non-smooth problem and thus, its best resolving strategy is critical regarding efficiency and times constraints. Three optimization strategies are evaluated in a synthetic real-scale scenario to select the more efficient one. Preliminary results are discussed and compared.