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Key Terms, Metrics, and Data Layer Definitions

Key terms, metrics, and data layer definitons used in Technosylva’s Wildfire Planning (FireSight) include:

Utility Infrastructure:

Risk and Probability:

Probability of Failure (POF)

Probability of Failure (POF) is the likelihood that a utility asset will experience a fault resulting in a spark or burning material under a given set of weather conditions. It represents the first link in the risk chain before Probability of Ignition (POI). The POF model looks at events that could potentially cause ignition (such as equipment failure, object contact, or vegetation contact) and integrates them with historical weather data to create dynamic circuit fragility curves. These curves consist of a static POF (representing the POF in windless conditions) and the dynamic exponential increase due to wind.

Probability of Ignition (POI)

Probability of Ignition (POI) is the likelihood that a failure resulting in spark or burning material at a utility asset results in an ignition. Environmental variables such as the National Fire Danger Rating System’s Ignition Component (IC) are used to generate a POI estimate. This analysis considers fuel variables along with wind speed to determine the likelihood of a wildfire starting from burning material on the ground. POI represents the second link in the risk chain after Probability of Failure (POF): given that an asset has failed, POI determines how likely that failure is to ignite a fire.

Conditional Risk

Conditional Risk quantifies the wildfire consequences associated with a utility asset, assuming an ignition occurs at that location. Conditional Risk answers the question: "if a fire starts here, what could happen?"

For each asset, FireSight evaluates seven risk metrics at 100-meter intervals along the line, taking the maximum value for each risk type to represent the worst-case consequence. These values are then aggregated across all simulated weather days to build a statistical distribution, from which FireSight derives percentiles, average, and standard deviation. For example, a cold, wet, low wind speed day may represent the lowest percentile while a hot, dry, windy day may represent represent the upper percentile.

The seven risk metrics considered for Conditional Risk are:

Expected Risk

Expected Risk quantifies the wildfire risk associated with a utility asset by integrating three factors: the Probability of Failure (POF), the Probability of Ignition (POI) given that failure, and the Conditional Risk of a resulting fire. Expected Risk reflects the full chain of events from equipment fault to fire consequence — making it the right metric when the question is "which assets pose the greatest overall risk of causing a damaging wildfire?"

For each simulated weather day, FireSight calculates the likelihood that an asset experiences an outage, the likelihood that the outage produces an ignition, and the potential fire consequences if ignition occurs. These daily values are combined and aggregated across all weather days to build a statistical distribution including percentiles, average, and standard deviation.

Risk Associated with an Ignition Location (RAIL)

Risk Associated with an Ignition Location (RAIL) assigns risk scores to specific utility assets based on their potential to ignite wildfires and the resulting consequences. A RAIL analysis incorporates:

The final output is a Conditional Risk and Expected Risk for each asset. By combining these components, RAIL generates asset-specific risk scores that enable utilities to prioritize mitigation efforts, operational decisions, and resource allocation based on the highest-risk equipment in their network.

Risk Associated with Value Exposure (RAVE)

Risk Associated with Value Exposure (RAVE) determines which locations have the highest risk from all surrounding assets, considering both fire spread exposure and demographic characteristics such as population, buildings, and infrastructure. By combining these components, RAVE generates risk scores across the landscape, linking them directly to electric utility assets as potential ignition sources.

RAVE differs from Risk Associated with an Ignition Location (RAIL), though the two are complementary. RAIL focuses on location-based consequences of a potential ignition, while RAVE incorporates environmental and community consequences. Together, RAVE and RAIL support composite asset risk metrics for both short-term operations and long-term mitigation planning.

Composite Risk Metrics

See the Using Composite Risk Metrics Tutorial for more information.

  • Fire Behavior Composite: A combined measure of Rate of Spread (ROS), Flame Length (FL), and Acres Burned that characterizes the intensity, speed, and overall magnitude of a potential fire.

  • Immediate Impacts Composite: A combined measure of Buildings Threatened, Population Impacted, and Acres Burned that quantifies the direct consequences a fire would have on nearby communities.

  • Suppression Difficulty Composite: A combined measure of Terrain Difficulty and Fire Behavior Composite that reflects how challenging a fire would be to control.

  • Utility Risk Composite: A combined measure of Suppression Difficulty and Immediate Impacts that identifies where infrastructure-caused wildfires pose the greatest direct risk to communities, supporting initial prioritization of system hardening and asset replacement.

  • Utility Risk Vegetation Composite: A composite measure of Utility Risk Composite and Canopy Risk focused on high-risk locations where vegetation management such as tree trimming and canopy thinning can most effectively reduce wildfire risk to both utility infrastructure and surrounding communities.

  • Utility Risk Fuel Loading Composite: A combined measure of Utility Risk Composite and Total Fuel Loading focused on high-risk locations where fuel reduction treatments can most effectively reduce wildfire risk, supporting strategic planning for fuel treatment programs and coordination with land management agencies.

Sub-Composite Risk Metrics

Fuels:

Terms like 2022B or 2030Fuels indicate the fuel scenario used for the risk metric. These scenarios indicate baseline or projected future vegetation and fuel conditions derived from the specific reference year, used as the current or future state fuel input for risk metric calculations. Risk metrics calculated against a future fuels scenario allow utilities to evaluate how wildfire risk may evolve over time and to assess whether planned mitigation activities are sufficient to offset increasing fuel loads or changing conditions. For more information on fuels, visit the Technosylva Fuels Glossary.

Other Terms and Abbreviations:

Statistics

  • E or underscore e: Expected

  • C or underscore c: Conditional

  • Min: Minimum

  • Max: Maximum

  • Stddev: Standard Deviation

  • Percentiles:

    • 20th: 20th percentile 

    • 40th: 40th percentile

    • 60th: 60th percentile

    • 80th: 80th percentile

    • 90th: 90th percentile

    • 95th: 95th percentile

    • 98th: 98th percentile

Other Terms

  • AIP Count: Count of the number of zones for an Auto Isolation Point(AIP).

  • Line Miles: Line miles per circuit.

  • PctCovered: Percent of a segment with covered conductors for risk mitigation. Derived using the formula sum(length covered)/sum(length).