DEVS-FIRE: Towards an Integrated Simulation Environment for Surface Wildfire Spread and Containment

Abstract

Simulating wildfire spread and containment remains a challenging problem due to the complexity of fire behavior. In this paper, the authors present an integrated simulation environment for surface wildfire spread and containment called DEVS-FIRE. DEVS-FIRE is based on the discrete event system specification (DEVS) and uses a cellular space model for simulating wildfire spread and agent models for simulating wildfire containment. The cellular space model incorporates real spatial fuels data, terrain data and temporal weather data into the prediction of wildfire behavior across both time and space. DEVS-FIRE is designed to be integrated with stochastic optimization models that use the scenario results from the simulation to determine an optimal mix of firefighting resources to dispatch to a wildfire. Preliminary computational experiments with fuel, terrain and weather data for a real forest demonstrate the viability of the integrated simulation environment for wildfire spread and containment.

Uncertainty and risk in wildland fire management: A review

“…Probabilistic models of fire occurrence and spread are also more easily validated against historical observation in that they can be compared to empirical statistics. Understanding how fire is likely to move across the landscape is extremely important for informing suppression and pre-suppression decision-making (e.g., Ntaimo et al, 2008;Ager et al, 2007;Finney, 2007;Lehmkuhl et al, 2007;Podur and Martell, 2007).…”

A comparative study of two alternative wildfire models, with applications to WSN topology control

“…the thermal energy balance along the propagating fire front, its generation on a burning area and its distribution to fractions of vertically convected energy, radiated energy and energy consumed for the combustion of the adjacent fuel. In order to tackle the spatiotemporal variability of the fire evolution over a realistic topography, due to variable fuel loads, humidity, ground slope, wind intensity and direction etc, the model follows the formalism and algorithmic structure deriving from the timed Cell-DEVS methods [11,12]. The fire domain is discretized in square cells (Figure 2a)) characterized by pertinent state parameters.…”

“…a 12 represents the receiver electronic equipments and depends only on the computation processing. Parameter γ is the path loss exponent and depends on the communication link between each sensor and is usually set to 2 or 4.…”

“…A system model of DEVS is described as a hierarchical composition of submodels each of them being behavioural or structural. Cell-DEVS formalism is a combination of DEVS and CA [12,13].…”

Simulation in the Cloud Using Handheld Devices

“…Examples of such phenomena include swarm movements in rural areas, in which a field-expert needs to update the simulation models at run-time with current observed data (such as the wind) [1], or civil protection scenarios, like forest fire spreading, in which fire troopers can use simulation models to predict fire propagation in realtime, based on their field observations and weather forecast information [2]. Indeed, due to their complexity, most simulations require high performance computing infrastructures.…”