Executive Summary
- Transformed reactive crisis management into proactive 72-hour forward decision support
- Integrated 25+ environmental data sources into a single operational digital model
- Enabled scenario simulations that predict flood impacts before they occur
- Reduced manual decision-making from 89% to automated recommendations
- Prepared infrastructure operators for CSRD compliance with audit-ready risk reports
1. Initial State: Reactive Crisis Management
Infrastructure operators managing roads, bridges, levees, and energy networks were making critical operational decisions based on historical data, spreadsheets, and reactive firefighting. When extreme weather events increased by 40% over the last decade, the gap between available data and actionable decisions became unacceptable.
Key Limitations
- Decisions based on outdated or incomplete environmental data
- No forward-looking scenario simulations
- 72-hour flood forecasting handled manually with limited accuracy
- Corporate Sustainability Reporting Directive (CSRD) and ESG reporting requirements creating compliance gaps
- Aging infrastructure (60% over 40 years old) with no predictive degradation models
- Crisis response instead of crisis prevention
While operational teams understood the risks, they lacked a system that could translate environmental data into specific, actionable recommendations with timing and priorities.
2. Project Objectives
The goal was to deploy a Decision OS that turns environmental data into operational decisions before crises occur, not another dashboard.
Core Objectives
- Enable 72-hour flood forecasting with building-level precision
- Integrate satellite, hydrological, terrain, and infrastructure data automatically
- Build a digital twin model of the physical operational environment
- Replace reactive responses with predictive scenario simulations
- Generate CSRD-ready risk reports with quantified financial impacts
- Provide specific recommendations: what to do, when, and in what order
3. Target Architecture: Decision OS for the Physical World
We redesigned the infrastructure around three-stage decision architecture, delivered as the Deceris Decision OS, built by u11d and now available worldwide. The first production deployment was built with IMGW-PIB, the Polish Institute of Meteorology and Water Management.
Data Integration Layer
The system automatically combines multiple data streams into a unified operational model:
- Satellite imagery for terrain and land use analysis
- Hydrological data from gauging stations and remote catchments
- Real-time weather patterns and precipitation forecasts
- Infrastructure asset data (bridges, levees, pump stations, roads)
- Historical event data spanning multiple decades
All data sources are connected automatically, without manual collection or reconciliation.
Physical World Model
Based on integrated data, the system builds an operational digital twin:
- Historical patterns and baseline behavior
- Current environmental state with real-time updates
- Change forecasts with probabilistic outcomes
- Asset vulnerability scoring by location and condition
Decision Engine
The user does not work with data. They work with decisions:
- Natural language questions get specific, numbered answers
- “Where will the flood be in 72 hours?” → 47 buildings at risk, 3 bridges to close, 2 pump stations in range, evacuation recommendation with priorities
- “What happens if the levee breaks at km 12.3?” → 2.3 km² flood zone, 890 buildings in range, 120M Polish złoty (PLN) estimated losses, reinforcement recommendation
- “What is the risk profile for the next 10 years?” → 3 high-risk locations, +40% probability increase by 2035, 45M PLN/yr estimated losses, CSRD report data ready
4. Predictive Modeling for Ungauged Catchments
A key differentiator of this implementation was the ability to model catchments without physical monitoring stations, one of the most challenging problems in hydrology.
Technical Approach
- Machine learning models trained on analogous catchments with full instrumentation
- Satellite-derived terrain and land cover data as primary inputs
- Ensemble forecasting combining multiple weather scenarios
- Uncertainty quantification for every prediction
Benefits
- 72-hour forecast horizon even in data-sparse regions
- Probabilistic risk ranges instead of single-point estimates
- Identifies which additional monitoring would most improve accuracy
- Enables infrastructure planning in greenfield areas
5. Scenario Simulation Engine
Manual scenario testing was replaced with instant operational simulations.
Simulation Capabilities
- “What-if” analysis for infrastructure modifications
- Levee failure cascade modeling across river systems
- Evacuation timing optimization based on flood propagation
- Heavy convoy route planning with trafficability constraints
- Asset degradation trajectories over 10-year horizons
Impact
- Every decision can be tested in simulation before execution
- Consequences are visible before they become reality
- Operational priorities emerge from quantitative analysis, not intuition
- Resource allocation optimized across multiple scenarios
6. Compliance and Reporting
The system generates audit-ready documentation for regulatory requirements.
CSRD and ESG Compliance
- Climate risk quantified in financial terms (PLN/yr losses)
- Asset-level risk scoring for sustainability reporting
- 10-year forward projections meeting regulatory expectations
- Action priorities with estimated risk reduction per intervention
Flood Directive Compliance
- Real-time flood zone mapping for emergency response planning
- Historical analysis demonstrating due diligence
- Infrastructure vulnerability documentation for regulators
7. Measurable Outcomes
Within the first deployment phase, infrastructure operators gained:
- 72-hour flood forecasting with building-level precision across entire operational regions
- 25+ integrated data sources unified in a single operational model
- Real-time scenario simulations running on operational data, not historical averages
- 90% reduction in manual decision-making through automated recommendation generation
- CSRD-ready reporting with quantified financial risk projections
- Evacuation planning support with prioritized sequencing and timing recommendations
8. Deployment Journey
Implementation follows a structured path from data integration to operational use.
Four-phase rollout
- Data Integration: connect satellite, hydrological and infrastructure data automatically, with quality validation and gap identification.
- Region Model: build the digital twin of the operational area, calibrated against known historical events and validated against holdout periods.
- First Scenarios: run simulations on operational data, test the question-and-answer interface with real operational teams, and measure prediction accuracy against subsequent events.
- Operational Use: continuous monitoring and forecasting, real-time recommendations for crisis response, and integration with existing operational workflows.
9. Ongoing Decision Support
After initial deployment, infrastructure operators continue under a decision-support model.
This includes
- Continuous model refinement as new data arrives
- Scenario library expansion based on operational priorities
- New asset types and operational domains as needed
- Regular accuracy audits against subsequent events
- Compliance reporting updates for regulatory deadlines
The Deceris Decision OS is a continuously evolving platform, not a one-time delivery.
Deceris is u11d's Decision OS for the physical world. First deployed with IMGW-PIB in Poland, it is now available as a turnkey platform for any infrastructure operator. Explore Deceris →
