Predicting how a hurricane cascades through US critical infrastructure

The problem

CISA is responsible for understanding what a disaster will do to the country's critical infrastructure before it does it. The hard part wasn't the modeling: the asset records needed to answer the question were spread across multiple federal and commercial data sources — each built separately, for different purposes, with no identifier in common.

Until those records are reconciled, no forecast is possible. A model can't predict what a storm does to a facility when the same facility appears four times under four names.

What we built

The first half of the work is ingesting, cleaning and normalizing heterogeneous data — the IP Gateway, All Hazards Analysis, NOAA, US Census, Harvard Business School economic data, and sector-specific asset holdings — and then resolving it into one coherent picture.

fastEM

An entity-matching engine built for this, combining probabilistic linkage (fastLink), spatial reasoning (Haversine distance with K-D tree indexing) and deterministic rules to link and de-duplicate infrastructure records that share no key. Geography does work here that string comparison cannot: two records naming the same site differently are still at the same coordinates.

Critical Asset List and Significant Asset Set

Rule sets applied over the resolved records, and continuously refined, across banking, communications, energy, transportation and healthcare — delivered in standard formats (CSV, RDS) the client's own analysts work with directly.

The forecast layer

Models that project the impact of a natural disaster onto the assets identified, and onto the economic clusters that depend on them.

The Infrastructure of Concerns Tool

A secure cloud-hosted GIS platform: interactive maps, filtering by sector and region, reporting, and predictive-impact visualization — plus the architecture documentation, function-level docs for fastEM and workflow guides needed to maintain it.

Why the record linkage comes first

Every model downstream inherits the quality of that reconciliation. If two records for one substation stay separate, the forecast counts it twice and overstates resilience. If two different substations get merged, it understates the exposure. Neither error announces itself in the output, so most of the engineering effort went into the matching engine.

The same capability — record linkage across systems that were never designed to be joined — recurs in our data-integration work whenever source systems do not align.

Result

A cloud platform that lets decision makers see, before a storm makes landfall, which critical assets and which economic clusters are in its path. The client reports improved readiness and preparedness, better resource allocation through prioritization and crew positioning, and reduced preparedness costs.