ETL and Data Ingestion
A disaster prevention system relies on a robust data layer: ingestion and fusion (ETL/ELT) of meteorological, hydrological, seismological, IoT/SCADA, radar, and satellite sources, as well as citizen reports and social media. This foundation operates sensor and telemetry management (station health, calibration, digital twin of the park) and a real-time observation module (nowcasting) to detect severe phenomena with low latency.
Risk Intelligence
Risk intelligence combines a library of hazard models (flood, landslides, earthquakes, fires, storm surge, heat waves, HazMat) with exposure and vulnerability models (land registry, population, critical infrastructure, lifelines). The output is synthesized into impact-based forecasts: risk maps and expected impact levels that feed thresholds and early warnings with rules/AI and multi-channel CAP delivery.
Dashboards
The operation requires a GIS/COP dashboard as a common view of the incident (thematic layers, geofences, safe routes, resource tracking), an ICS-aligned incident management module, scenario simulations (evacuations, shelters, road closures), and resource logistics (inventories, crews, PPE). Public communication is provided via SMS/cell broadcast, sirens, radio, apps, and social media with accessible and multilingual messages.
Assessment
After the event, the system incorporates rapid damage assessment (RDNA) with field, drone, and satellite apps; post-event analytics (KPIs, alert/response times, costs) and continuous improvement. All of this is supported by cybersecurity and resilience (zero-trust, DR/BCP, immutable backups), interoperability and APIs (OGC, EDXL, NIEM), data governance and compliance (privacy, auditing, roles), and an ongoing training and drill program. High-value options include risk finance (catastrophe insurance/bonds, parametric triggers) and communications relay for continuity in areas without coverage.
