An attacker‐defender model for analyzing the vulnerability of initial attack in wildfire suppression

Abstract

Wildfire managers use initial attack (IA) to control wildfires before they grow large and become difficult to suppress. Although the majority of wildfire incidents are contained by IA, the small percentage of fires that escape IA causes most of the damage. Therefore, planning a successful IA is very important. In this article, we study the vulnerability of IA in wildfire suppression using an attacker-defender Stackelberg model. The attacker's objective is to coordinate the simultaneous ignition of fires at various points in a landscape to maximize the number of fires that cannot be contained by IA. The defender's objective is to optimally dispatch suppression resources from multiple fire stations located across the landscape to minimize the number of wildfires not contained by IA. We use a decomposition algorithm to solve the model and apply the model on a test case landscape. We also investigate the impact of delay in the response, the fire growth rate, the amount of suppression resources, and the locations of fire stations on the success of IA.KEYWORDS attacker-defender model, critical infrastructure, initial attack, Stackelberg game, vulnerability assessment, wildfire management 1 120

Geospatial Modeling of Containment Probability for Escaped Wildfires in a Mediterranean Region

“…Accordingly, predicting where we can restrict fire growth is decisive, and current decision support systems aim to integrate estimates of wildfire containment probability (Wei, Thompson, Scott, O'Connor, & Dunn, 2019). The common strategies to produce such probabilities rely on binary statistical models (O'Connor, , modeling algorithms (Eghbal, Hugh, & Aaron, 2018), or expert-informed indices (Rodríguez y Silva, Molina Martínez, & González-Cabán, 2014). These models leverage historical fire perimeters and landscape features such as ground accessibility, presence of unburnable barriers, vegetation impedance, local fire danger conditions, or wildfire behavior (Holsinger, Parks, & Miller, 2016;Narayanaraj & Wimberly, 2011;O'Connor et al, 2017;Price, Borah, & Maier, 2013;Rodríguez y Silva et al, 2014).…”

Cell2Fire: A Cell-Based Forest Fire Growth Model to Support Strategic Landscape Management Planning

“…In addition, Cell2Fire provides access to node influence metrics aiming to assess the influence of each node in the network and the potential impact of removing/treating them, sharing concepts of previous works such as Acuna et al (2010) andTroncoso et al (2016). Examples of mathematical models to identify and optimize fuel treatment locations, incorporate the role of land-owners in fuel treatment activities via a cost-share approach, and maximize the effectiveness of the initial attack in the context of wildfire suppression can be found in Rashidi et al (2017), Rashidi et al (2018), andBhuiyan et al (2019). Finally, a random treatment heuristic is included where available cells are selected at random in the landscape, establishing a baseline for comparison purposes.…”

Fuel management operations planning in fire management: A bilevel optimisation approach

“…Although not directly relevant to this review, it is still interesting to mention that, in a subsequent article, Rashidi et al (2018a) propose the vulnerability assessment of the initial attack problem (VAIAP), which extends PTMP in multiple ways and shifts the focus from fuel management to suppression. In VAIAP, the attacker can locate multiple ignition points, while the defender takes two types of allocation decision: the pre-attack location of suppression resources to fire stations, and the post-attack dispatch of these resources to control the fires.…”