> ## Documentation Index
> Fetch the complete documentation index at: https://resq-dependabot-github-actions-github-actions-478e18be3d.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Models

<a id="resq_mcp.pdie.models" />

# resq\_mcp.pdie.models

PDIE domain models for the ResQ MCP server.

<a id="resq_mcp.pdie.models.annotations" />

## annotations

<a id="resq_mcp.pdie.models.datetime" />

## datetime

<a id="resq_mcp.pdie.models.Literal" />

## Literal

<a id="resq_mcp.pdie.models.BaseModel" />

## BaseModel

<a id="resq_mcp.pdie.models.Field" />

## Field

<a id="resq_mcp.pdie.models.VulnerabilityMap" />

## VulnerabilityMap Objects

```python theme={null}
class VulnerabilityMap(BaseModel)
```

Static vulnerability assessment data for a geographic sector.

Part of PDIE (Predictive Disaster Intelligence Engine) system.
Contains precomputed risk factors, infrastructure data, and population
metrics used for predictive disaster modeling and resource allocation.

**Attributes**:

* `sector_id` - Sector identifier this map applies to.
* `population_density` - Human population density category.
* `critical_infrastructure` - List of critical facilities (e.g., "hospital", "power-substation").
* `flood_risk` - Flood vulnerability score (0.0 to 1.0).
* `fire_risk` - Fire vulnerability score (0.0 to 1.0).
* `last_updated` - UTC timestamp of last data update (auto-generated).

**Notes**:

Risk scores are precomputed from historical data, terrain analysis,
and infrastructure density. Updated periodically via GIS integration.

<a id="resq_mcp.pdie.models.VulnerabilityMap.sector_id" />

#### sector\_id

<a id="resq_mcp.pdie.models.VulnerabilityMap.population_density" />

#### population\_density

<a id="resq_mcp.pdie.models.VulnerabilityMap.critical_infrastructure" />

#### critical\_infrastructure

<a id="resq_mcp.pdie.models.VulnerabilityMap.flood_risk" />

#### flood\_risk

<a id="resq_mcp.pdie.models.VulnerabilityMap.fire_risk" />

#### fire\_risk

<a id="resq_mcp.pdie.models.VulnerabilityMap.last_updated" />

#### last\_updated

<a id="resq_mcp.pdie.models.PreAlert" />

## PreAlert Objects

```python theme={null}
class PreAlert(BaseModel)
```

Probabilistic disaster forecast from LSTM/GNN predictive models.

Part of PDIE system. Generated by machine learning models that analyze
weather patterns, sensor data, and historical trends to predict potential
disasters before they occur. Enables proactive resource positioning.

**Attributes**:

* `alert_id` - Unique alert identifier (e.g., "PRE-A1B2C3D4").
* `sector_id` - Target sector for the prediction.
* `predicted_disaster_type` - Expected disaster type (e.g., "wildfire", "flood").
* `probability` - Forecast confidence (0.0 to 1.0).
* `forecast_horizon_hours` - Time until predicted event (hours from now).
* `vulnerability_context` - Associated sector vulnerability data.
* `generated_at` - UTC timestamp of forecast generation (auto-generated).

**Example**:

> > > alert = PreAlert(
> > > ...     alert\_id="PRE-123ABC",
> > > ...     sector\_id="Sector-1",
> > > ...     predicted\_disaster\_type="wildfire",
> > > ...     probability=0.85,
> > > ...     forecast\_horizon\_hours=12,
> > > ...     vulnerability\_context=vuln\_map
> > > ... )

<a id="resq_mcp.pdie.models.PreAlert.alert_id" />

#### alert\_id

<a id="resq_mcp.pdie.models.PreAlert.sector_id" />

#### sector\_id

<a id="resq_mcp.pdie.models.PreAlert.predicted_disaster_type" />

#### predicted\_disaster\_type

<a id="resq_mcp.pdie.models.PreAlert.probability" />

#### probability

<a id="resq_mcp.pdie.models.PreAlert.forecast_horizon_hours" />

#### forecast\_horizon\_hours

<a id="resq_mcp.pdie.models.PreAlert.vulnerability_context" />

#### vulnerability\_context

<a id="resq_mcp.pdie.models.PreAlert.generated_at" />

#### generated\_at