Coverage metrics
Coverage measures how often predictable outages had a prior signal within the configured radius and lead window.
Forecast accountability
Outage prediction should measure itself.
Forecast accountability tracks whether predictions were timely, useful, and trustworthy enough for operational decisions.
prediction accuracy
outage forecast validation
model trust
Lead time
Lead time measures whether forecasts arrived early enough to support staging, briefing, or monitoring.
False positives and false negatives
False positives and misses help operators understand model behavior and where data quality or thresholds need refinement.
Model trust
GeoGridIQ tracks artifact availability, feature contract compatibility, temporal validation, leakage controls, and fallback status.
Frequently asked questions
Direct answers for operators, planners, and AI search.
Why is forecast accountability important?
It prevents prediction dashboards from becoming unmeasured claims and helps teams improve models over time.
Does a rules fallback mean failure?
No. Fallback is a safety path when model trust or data quality is insufficient.
Related GeoGridIQ resources
Continue exploring utility intelligence.
Documentation
Read GeoGridIQ documentation for platform overview, data sources, prediction engine, GIS engine, weather intelligence, NDVI, and crew optimization.
Open Data Reports
Public utility intelligence reports covering Quebec outage risk, vegetation threats, storm impact, and critical infrastructure exposure.
Outage Prediction Platform
GeoGridIQ helps utilities forecast outage risk using weather intelligence, vegetation analytics, historical outages, GIS context, and model confidence scoring.
Vegetation Risk Analysis
Monitor vegetation outage risk with NDVI analysis, satellite context, GIS layers, and utility risk scoring.