Automation Workflow for Flood Inundation Mapping and Evacuation Route Planning

Manual flood modeling can take GIS teams 6-12 hours post-event, delaying critical decisions. An automated workflow ingests real-time rainfall and river gauge data, runs hydraulic models on-demand using high-resolution LiDAR DEMs, and publishes dynamic inundation maps within 15-20 minutes. This 95% reduction in cycle time allows emergency managers to visualize threat evolution as the storm unfolds, not after it has passed.

Static evacuation plans fail when key roads are flooded. This workflow integrates live inundation maps with traffic and road network data, using graph-based agents to continuously simulate and rank optimal evacuation paths. It automatically identifies at-risk communities and pushes route updates to public alert systems (e.g., IPAWS) and traffic management centers, reducing evacuation planning latency from 2+ hours to near-real-time and improving traffic flow efficiency by an estimated 25-40% during crises.

Manual flood response pulls GIS analysts, hydrologists, and emergency planners into high-stress, repetitive tasks. Automating data ingestion, model execution, and map generation frees up 70-80% of a specialist's time during an event, allowing them to focus on exception handling and strategic coordination. For a regional agency, this can equate to leveraging a 5-person team to perform the work of a 25-person manual operation during peak crisis periods.

Earlier, more accurate flood intelligence enables proactive measures like deploying sandbags, closing floodgates, or pre-emptively shutting off utilities in specific zones. By preventing water ingress into substations, wastewater plants, and transportation corridors, a custom workflow can reduce preventable infrastructure damage by 15-30%. For a municipality, this directly translates to millions in avoided repair costs and faster post-event recovery.

Dispatchers and field units often operate with outdated maps. This workflow integrates directly with Computer-Aided Dispatch (CAD) and emergency response platforms (e.g., RapidSOS, Esri Field Maps), providing units with live flood layers and safe routing on mobile devices. This reduces responder risk and improves mission efficiency by ensuring personnel and equipment are not inadvertently directed into flooded areas, a critical factor in swift water rescue operations.

Federal grants (FEMA, EPA) require documented evidence for disaster declarations and mitigation projects. An automated workflow creates a timestamped, versioned audit trail of all data inputs, model parameters, and output maps. This defensible record cuts post-event reporting and grant application preparation from weeks to days, accelerating reimbursement cycles and strengthening the case for future mitigation funding based on proven, data-driven operations.

This core agent ingests real-time rainfall data, river gauge readings, and high-resolution LiDAR elevation models (DTMs). It executes 2D hydraulic simulations (e.g., using HEC-RAS or LISFLOOD-FP models) to predict flood extent, depth, and velocity. The agent runs continuously, updating forecasts as new data arrives, and publishes georeferenced inundation polygons to a central geospatial database for downstream consumption.

An overlay agent intersects the dynamic inundation layers with static GIS layers containing building footprints, critical infrastructure (hospitals, schools), and population density data. It performs a spatial join to generate a prioritized list of affected assets and estimated populations at risk. This list is tagged with severity (e.g., depth > 1m) and routed to the alerting system and evacuation planner.

This agent consumes the inundation map and road network data (including real-time traffic feeds and known closures). Using graph-based algorithms (e.g., Contraction Hierarchies), it calculates multiple optimal evacuation paths from risk zones to safe shelters, dynamically rerouting around flooded or congested roads. It outputs turn-by-turn instructions and expected travel times, formatted for integration with public alerting and navigation apps like Waze or Google Maps.

A central orchestrator (built with LangGraph or similar) manages the workflow, handles exceptions, and triggers integrations. It pushes approved inundation maps and evacuation plans to Emergency Management platforms (e.g., Everbridge, Veoci), traffic management systems (SCADA, ATMS), and public warning systems (IPAWS). It also manages approval gates for major alert escalations, ensuring human oversight is maintained for high-stakes decisions.

Critical for operational trust, this component provides a dashboard showing data pipeline health, model confidence scores, and alert delivery status. It logs all agent decisions, data sources used, and human overrides for auditability. It also monitors the performance of the hydraulic model against ground-truth observations (e.g., flood cameras, sensor readings) to trigger alerts for model drift or retraining needs.

A typical build uses a cloud-native, event-driven architecture. Sensor data is ingested via APIs or message queues (Kafka). Hydraulic models run in containerized, GPU-accelerated environments. Agents are implemented as microservices, orchestrated via LangGraph for state management. Geospatial data is served via PostGIS/GeoServer. The front-end is a React-based situational awareness dashboard integrated with mapping libraries (Cesium, MapLibre). Deployment is phased, starting with a high-value watershed.

The workflow's trigger layer ingests live river gauge readings (USGS, NOAA APIs), quantitative precipitation estimates (QPE) from radar, and forecast models (GFS, HRRR). Agents validate data quality, handle API failures, and normalize feeds into a unified spatiotemporal grid, providing the foundational inputs for hydraulic simulation with sub-hourly latency.

Inundation accuracy depends on integrating LiDAR-derived digital elevation models (DEMs) at 1m resolution or better with building footprint layers and critical infrastructure GIS data. Agents manage versioning of these large raster/vector datasets, perform necessary hydro-conditioning (burning streams, removing bridges), and ensure the model terrain reflects current land use, which is often sourced from municipal or state GIS portals.

Core computational agents orchestrate 2D hydraulic models (e.g., HEC-RAS 2D, LISFLOOD-FP) on cloud HPC resources. They parameterize simulations with ingested rainfall/flow data, execute parallel runs for multiple scenarios, and post-process outputs to generate depth and velocity rasters. This layer is where the physical flood dynamics are computed before being passed to impact and routing logic.

For dynamic evacuation routing, the workflow integrates with real-time traffic data (INRIX, HERE) and traffic signal control systems (SCATS, Siemens) via APIs. Route optimization agents ingest flood depth rasters, road closure data, and live speeds to calculate least-time egress paths, simulating bottlenecks and providing turn-by-turn guidance that can be pushed to public alerting apps or variable message signs.

Validated inundation maps and recommended evacuation zones are formatted and pushed via ESI or CAP standards to Integrated Public Alert & Warning System (IPAWS) aggregators and Common Operational Picture (COP) platforms like Cesium or Esri's ArcGIS Velocity. This integration ensures alerts reach EOCs, 911 dispatch, and public mobile devices with the geospatial context needed for coordinated response.

To protect assets and inform shutdown procedures, the workflow integrates with utility SCADA networks and facility GIS. Agents cross-reference flood extents with substation locations, water treatment plants, and hospital footprints. Automated alerts and predicted impact summaries are routed to relevant operational dashboards in systems like OSIsoft PI or Schneider Electric EcoStruxure, enabling preemptive action.