Abstract: A service inputs wind data and utility data corresponding to a utility component into a first machine learning model to determine an outage risk prediction representing a probability that the utility component will have an outage. The service determines a probability of ignition at a vicinity of the utility component, and determines a set of wildfire impact measurements by simulating a wildfire in the vicinity of the utility component. The service inputs the outage risk prediction, the probability of ignition, and the set of wildfire impact measurements into a second machine learning model, and receives as output from the second machine learning model, a catastrophic wildfire risk score corresponding to the utility component. The service outputs a graphical representation on a dashboard representing the catastrophic wildfire risk score.

Abstract: A service determines a Hot Dry Windy (HDW) index for a vicinity around a utility component by inputting atmospheric conditions and weather conditions in the vicinity into a HDW model and receiving the HDW index as output from the HDW model. The service determines an Energy Release Component (ERC) percentile by inputting fuel loading and combustibility characteristics into an ERC model and receiving, as output from the ERC model, the ERC percentile. The service aggregates the HDW index and the ERC percentile into a modified HDW (mHDW) metric, inputs forecasted fire characteristics for the vicinity and the HDW index into a machine learning model, and receives as output from the machine learning model a likelihood of a fire growing to a threshold size. The service displays fire risk metric for the vicinity based on the likelihood of the fire growing to the threshold size.

Abstract: A service inputs wind data and utility data corresponding to a utility component into a first machine learning model to determine an outage risk prediction representing a probability that the utility component will have an outage. The service determines a probability of ignition at a vicinity of the utility component, and determines a set of wildfire impact measurements by simulating a wildfire in the vicinity of the utility component. The service inputs the outage risk prediction, the probability of ignition, and the set of wildfire impact measurements into a second machine learning model, and receives as output from the second machine learning model, a catastrophic wildfire risk score corresponding to the utility component. The service outputs a graphical representation on a dashboard representing the catastrophic wildfire risk score.

Abstract: A service inputs building characteristics for a building into a machine learning model configured to output a probability that a given building will be lost should a fire reach the building, and receives as output from the model a building loss factor for the building. The service determines determining an exposure measurement for the building by performing simulations, over a plurality of candidate environmental parameters, of whether a simulated fire would encroach on the building. The service determines a building damage potential measurement based on the building loss factor, the exposure measurement, and an intensity measurement, and generates for display a graphical user interface showing fire risk for the building based on the building damage potential measurement.