Applying a Dynamic Data Driven Genetic Algorithm to Improve Forest Fire Spread Prediction

Abstract

Abstract. This work represents the first step toward a DDDAS for Wildland Fire Prediction where our main efforts are oriented to take advantage of the computing power provided by High Performance Computing systems to, on the one hand, propose computational data driven steering strategies to overcome input data uncertainty and, on the other hand, to reduce the execution time of the whole prediction process in order to be reliable during real-time crisis. In particular, this work is focused on the description of a Dynamic Data Driven Genetic Algorithm used as steering strategy to automatic adjust certain input data values of forest fire simulators taking into account the underlying propagation model and the real fire behavior.

An Architecture for Adaptive Robust Modelling of Wildfire Behaviour under Deep Uncertainty

“…It relies on an array-based and semantically enhanced application of the dynamic data driven application systems [22,23] for addressing dynamic simulations under an array of multiple fuel models, meteorological disturbances and control strategies for mitigating fire damages.…”

“…Therefore, time has been proposed as an explicit variable inside fire models into the square of fire factors [53,54]. Dynamic Data Driven Application Systems (DDDAS) [23] programmatically exploit data-driven feedbacks for updating the information used as model input (e.g. adaptive estimates of required information via specialised data transformation models, D-TM [12,13,15]).…”

“…The concept of Dynamic Data Driven Application Systems (DDDAS) [22,23] is exploited along with a Partial Open Loop Feedback Control (POLFC) approach [24] for mitigating the overall costs associated with fire events by means of an adaptive use of timely available information. Preliminary prototypes of the proposed architecture have been discussed in [9,25].…”

Computational Steering Strategy to Calibrate Input Variables in a Dynamic Data Driven Genetic Algorithm for Forest Fire Spread Prediction

“…In a previous work [6], the authors introduced the data assimilation process needed for this two stage prediction method and an analytical steering strategy for the Calibration Stage was also described.…”

“…Taking into account these facts and knowing slope characteristics (as we had mentioned in a previous paragraph), we could combine slope and real fire spread in order to obtain wind values, those which are necessary for achieving the observed fire spread [6]. For this purpose, an analytical steering strategy for the Calibration Stage was introduced in [6].…”