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Helpful Places

Government of Canada – DTPR AI Register

AI-Generated National Flood Susceptibility Mapping

Ecology · Planning & Decision-making

What it collects

About a measurement
Anonymized data
  • Climate variables (e.g. precipitation, temperature) and hydro-geomorphological variables (e.g. slope, drainage, soil type) used as predictors in the flood susceptibility model. These are environmental sensor and geospatial measurements with no individual person attached.
About a place
Anonymized data
  • Historic flood event locations across Canada, used to identify spatial patterns of flooding. Data describes where past floods occurred rather than identifying any individual.
Run by
Natural Resources Canada (NRCan)
Where
No fixed location
Kept
Not stated by the Helpful Places.
Shared with
Accountable organization
Your copy
You cannot see the data it holds about you. What you can do

What it is for

EcologyPlanning & Decision-making

This system uses machine learning to produce a national map of Canada showing how likely each area is to experience flooding, based on historical flood data and climate and hydro-geomorphological variables. It is used by Government of Canada employees for planning and analysis purposes. Members of the public are not directly informed when this tool influences decisions. The model is an ensemble machine learning approach trained on patterns from past flood events.

What it collects and what happens to it

Data taken in

About a measurement
Anonymized data
  • Climate variables (e.g. precipitation, temperature) and hydro-geomorphological variables (e.g. slope, drainage, soil type) used as predictors in the flood susceptibility model. These are environmental sensor and geospatial measurements with no individual person attached.
About a place
Anonymized data
  • Historic flood event locations across Canada, used to identify spatial patterns of flooding. Data describes where past floods occurred rather than identifying any individual.

Processing

Classification & Prediction
  • An ensemble machine learning model classifies geographic areas into flood susceptibility levels based on historical flood event patterns and climate and hydro-geomorphological predictor variables.

What it does

Deciding (Analytical AI)
Human decides
  • The system scores and classifies geographic areas by flood susceptibility using an ensemble machine learning model. Outputs are a ranked susceptibility map used by GC employees as an advisory input to planning decisions.

Outputs

About a place
Anonymized data
  • A national map of Canada showing flood susceptibility scores by geographic area. The output is a spatial index of flood likelihood — not tied to any individual — used by GC employees for planning purposes.
A recommendation or prediction
Anonymized data
  • The susceptibility index provides an advisory ranking of flood-prone areas to support planning decisions. GC employees interpret and apply the outputs; no automated binding decision is made by the model itself.

Run by

Natural Resources Canada (NRCan)
  • Natural Resources Canada (NRCan) is the federal department responsible for developing and deploying this flood susceptibility mapping system for use by Government of Canada employees.

Government of Canada AI Register — Flood Susceptibility Index

Built by

Government of Canada
  • The system was developed by the Government of Canada, indicating in-house development rather than a third-party commercial vendor.

Government of Canada AI Register — Flood Susceptibility Index

Kept for

Not stated by the Helpful Places.

Shared with

Available to the accountable organization
  • Outputs are accessible to Government of Canada employees for planning and analysis. The register identifies GC employees as the primary users.
Not available to me
  • Members of the public do not have a direct interface to query the system or access individualized outputs about their properties. The AI use has not been disclosed to the public per the register.

Stored

Not stated by the Helpful Places.

How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the Helpful Places, not the vendor.

Your rights

  • Right to Algorithmic TransparencyThe methodology underlying the Flood Susceptibility Index is publicly documented in GeoScan publication R=329493. However, the Government of Canada AI Register confirms that AI use has not been disclosed to affected members of the public. Citizens may submit access to information requests to Natural Resources Canada for further details.

Risks and safeguards

  • Civil liberties harmAI-based flood susceptibility mapping may influence land-use, insurance, or development decisions that affect property rights and community wellbeing without public notice (the register confirms AI use is not disclosed to users).Safeguard: The system targets aggregate geographic analysis rather than individuals; outputs are advisory to GC employees only. The methodology is published via GeoScan for independent review.