← Decision Patterns Monitoring & Data
What problem does this solve?
Reacting to events after they happen costs lives and money. Decision-makers need advance warning of floods, droughts, and system failures.
How it works
Computer models combine real-time sensor data with weather forecasts and historical patterns to predict what will happen hours or days ahead, giving time to prepare.
Typical infrastructure
Hydrological models, weather prediction systems, computing infrastructure
Typical monitoring
Forecast accuracy tracking, model calibration against observed events
Strengths
Extends warning lead times; supports proactive rather than reactive decisions; improves with more data over time
Trade-offs
Models are only as good as their input data; extreme events may exceed model assumptions; requires specialist expertise to build and maintain
Related use cases
Operational scenarios where this pattern is applied:
Case studies
Real-world examples of this pattern in action: