Modeling wildfire propagation with the stochastic shortest path: A fast simulation approach. (August 2016)

• Record Type: Journal Article
• Authors: Hajian, Mohammad; Melachrinoudis, Emanuel; Kubat, Peter
• Is Part Of: Environmental modelling & software. Volume 82(2016:Aug.)
• Page Start: 73
• Page End: 88
• Publication Date: 2016-08
• Subjects: Shortest path -- Stochastic modeling -- Wildfire propagation -- Network size reduction; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.03.012
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Wildfires have significant environmental and economic effects. Since containment of wildfires involves deciding under tight time constraints, there is an increasing need for accurate yet computationally efficient wildfire prediction models. We consider the problem of finding the fire traversal time across a landscape considering wind speed as an unpredictable phenomenon. The landscape is represented as a graph network and fire propagation time is modeled as the Stochastic Shortest Path problem. Monte-Carlo simulation is utilized to determine the fire travel-time distribution. A network size reduction methodology is introduced to quicken the simulation time by eliminating the unimportant parts of the network. This methodology is implemented in Java to simulate the wildfire propagation on a study area located in Massachusetts. This method shows the capability of substantially reducing the simulation time without affecting prediction accuracy, enabling the algorithm to serve as a fast and reliable tool for fire prediction. Highlights: Fire traversal time is modeled as the Stochastic Shortest Path. The model accounts for the variability of the wind speed. A network size reduction methodology is introduced to speed up the simulation. A case study in Massachusetts confirms that the reduction methodology is accurate and efficient.