Catastrophic wildfire index for forecasting utility-caused wildfires

A non-transitory computer-readable medium comprising memory with instructions encoded thereon that, when executed, cause one or more processors to perform operations, the instructions comprising instructions to:

• input wind data and utility data corresponding to a utility component into a first machine learning model, wherein the first machine learning model is trained using training examples indicating at least a representation of wind speed and attributes of a given utility component as paired with a label indicating whether or not an outage was experienced, and wherein as new outages are experienced, the first machine learning model is re-trained using new training examples indicating, for the new outages, at least a representation of new wind speed and new attributes of a utility component that experienced a new outage;
• receive, as output from the first machine learning model, an outage risk prediction representing a probability that the utility component will have an outage;
• determine a probability of ignition at a vicinity of the utility component;
• determine a set of wildfire impact measurements by simulating a wildfire in the vicinity of the utility component;
• input the outage risk prediction, the probability of ignition, and the set of wildfire impact measurements into a second machine learning model;
• receive, as output from the second machine learning model, a catastrophic wildfire risk score corresponding to the utility component;
• and
• output a graphical representation on a dashboard representing the catastrophic wildfire risk score.

A system comprising:

• memory with instructions encoded thereon;
• and
• one or more processors that, when executing the instructions, are caused to perform operations comprising:
• inputting wind data and utility data corresponding to a utility component into a first machine learning model, wherein the first machine learning model is trained using training examples indicating at least a representation of wind speed and attributes of a given utility component as paired with a label indicating whether or not an outage was experienced, and wherein as new outages are experienced, the first machine learning model is re-trained using new training examples indicating, for the new outages, at least a representation of new wind speed and new attributes of a utility component that experienced a new outage;
• receiving, as output from the first machine learning model, an outage risk prediction representing a probability that the utility component will have an outage;
• determining a probability of ignition at a vicinity of the utility component;
• determining a set of wildfire impact measurements by simulating a wildfire in the vicinity of the utility component;
• inputting the outage risk prediction, the probability of ignition, and the set of wildfire impact measurements into a second machine learning model;
• receiving, as output from the second machine learning model, a catastrophic wildfire risk score corresponding to the utility component;
• and
• outputting a graphical representation on a dashboard representing the catastrophic wildfire risk score.