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<p>This paper presents a novel method for assigning ignition locations in Fire Following Earthquake (FFE) modeling. FFE modeling comprises three key stages: ignition, spread, and suppression. In the ignition phase, the number of ignitions per city district is traditionally estimated using empirical relationships, with ignitions randomly distributed across districts—introducing significant uncertainty. The proposed method addresses this limitation by distributing ignitions based on district-specific fire risk levels, thereby reducing uncertainty. Each district is evaluated and scored based on fire risk, using a set of weighted influential factors to calculate a risk score. A random component, weighted at 20%, is combined with these scores to determine ignition probabilities, which guide the allocation of ignitions across districts. This 20% random component accounts for uncertainties arising from unconsidered factors, such as human error. The proposed method is applied to FFE modeling in the Tehran metropolis, capital of Iran, employing Hazus methodology with assuming no suppression capability by the city’s fire departments. Five scenarios, varying in magnitude, location, and activated fault, were analyzed. In the worst-case scenario, results indicated up to 73 ignitions, potentially affecting approximately 178,558 buildings and requiring 8819.08 cubic meters of water for suppression.</p>

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An Enhanced Method for Assigning Ignition Locations in Fire Following Earthquake Modeling

  • 期刊论文

  • 发表日期：

    2026年2月16日

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  • Ali Tanoumand,
  • Mohammadreza Mashayekhi

摘要

This paper presents a novel method for assigning ignition locations in Fire Following Earthquake (FFE) modeling. FFE modeling comprises three key stages: ignition, spread, and suppression. In the ignition phase, the number of ignitions per city district is traditionally estimated using empirical relationships, with ignitions randomly distributed across districts—introducing significant uncertainty. The proposed method addresses this limitation by distributing ignitions based on district-specific fire risk levels, thereby reducing uncertainty. Each district is evaluated and scored based on fire risk, using a set of weighted influential factors to calculate a risk score. A random component, weighted at 20%, is combined with these scores to determine ignition probabilities, which guide the allocation of ignitions across districts. This 20% random component accounts for uncertainties arising from unconsidered factors, such as human error. The proposed method is applied to FFE modeling in the Tehran metropolis, capital of Iran, employing Hazus methodology with assuming no suppression capability by the city’s fire departments. Five scenarios, varying in magnitude, location, and activated fault, were analyzed. In the worst-case scenario, results indicated up to 73 ignitions, potentially affecting approximately 178,558 buildings and requiring 8819.08 cubic meters of water for suppression.