# Levee Flood Risk Screener — USACE National Levee Database (`malonestar/usace-levee-flood-risk-screener`) Actor Check if a property sits behind a US levee and how much to trust it. Screens assets or a state/county inventory against USACE's National Levee Database with a composite risk score from FEMA accreditation, overtopping chance, rehab status, warning system and egress routes. - **URL**: https://apify.com/malonestar/usace-levee-flood-risk-screener.md - **Developed by:** [Kyle Maloney](https://apify.com/malonestar) (community) - **Categories:** Agents, Developer tools, Real estate - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.40 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## USACE Levee Flood Risk Screener — National Levee Database This actor answers the question a FEMA flood-zone lookup **cannot**: > **"Is there a levee between this property and the water — and can I actually trust it?"** It screens locations (or a state/county inventory) against the US Army Corps of Engineers **National Levee Database (NLD)** — the authoritative federal registry of ~30,000 leveed areas — and computes an editorial **levee risk score** from the attributes that actually predict levee failure risk: FEMA accreditation status, overtopping chance, PL 84-99 rehab-program enrollment, warning-system presence, and mapped egress routes. This is deliberately **not** a flood-zone lookup. A property can sit in FEMA Zone X (no flood insurance required) purely because it's "protected" by a levee FEMA itself rates **Non-Accredited** — meaning FEMA does not actually trust that levee to hold back a 1%-annual-chance flood. That gap is the whole point of this actor. ### Who it's for - **Insurance & underwriting teams** — screen a book of insured properties for hidden levee-dependent flood exposure that flood-zone data alone misses. - **Real-estate & title due diligence** — check a parcel before close; a Non-Accredited levee is a material disclosure risk. - **Lenders & mortgage risk** — flag levee-protected collateral that FEMA maps understate. - **Municipal & emergency management** — rank levees in your county by risk to prioritize inspection and rehab funding requests. - **AI agents** — a clean, chainable "levee check" tool: coordinates in, structured risk verdict out. ### Two modes (auto-detected from input) #### 1. Assets mode — screen specific points Set `assets` (a list of `{lat, lon, label}`). The actor runs a server-side point-in-polygon query against every NLD leveed-area polygon for each point. One row out per asset — **assets with no levee overlap are still returned** (`behind_levee: false`) so a batch never silently drops rows. ```json { "assets": [ { "lat": 45.903, "lon": -111.53, "label": "Gallatin County, MT (Madison River levee)" }, { "lat": 38.075, "lon": -102.28, "label": "Granada, CO (Granada Right Levee)" } ] } ```` #### 2. Inventory mode — rank levees in an area Leave `assets` empty and set `states` and/or `counties` instead. The actor pages the NLD, computes a risk score for every matching leveed area, and outputs the **top `maxResults` ranked by leveeRiskScore descending** — the worst levees in your area of interest, first. ```json { "states": ["CO"], "counties": [], "maxResults": 100 } ``` ### Output fields | Field | Meaning | |---|---| | `asset_label`, `asset_lat`, `asset_lon` | Your input point, echoed back (assets mode) | | `behind_levee` | True if the point falls inside a mapped leveed area (assets mode) | | `leveed_area_count` | How many leveed-area polygons contain the point (assets mode) | | `rank` | 1 = highest risk in the fetched set (inventory mode) | | `system_name`, `system_id` | Official NLD system name / ID | | `states`, `counties`, `community_names` | Where the leveed area sits (systems can span multiple states) | | `sponsors`, `sponsor_type` | Who owns/maintains the levee | | `fema_accreditation_rating` | FEMA's certification status for the levee | | `non_accredited_flag` | True unless the rating is exactly "Accredited Levee System" | | `overtopping_ace` | Parsed annual chance of overtopping, when known | | `rehab_program_status` | PL 84-99 federal rehab enrollment: Active / Inactive / Not Enrolled | | `warning_system` | Whether a flood warning system is documented | | `egress_number` | Mapped evacuation routes out of the leveed area (0 = none on file) | | `leveed_area_sq_mi`, `flood_sources` | Size and the water body it protects against | | `leveeRiskScore` | 0-100 editorial composite risk score (see below) | | `checked_at`, `disclaimer`, `source_url`, `error` | Run metadata and advisory notice | Invalid asset points produce an `error` row and the run continues — one bad coordinate never fails a large batch. ### How `leveeRiskScore` is computed `leveeRiskScore` is an **editorial model**, not a FEMA or USACE figure: ``` leveeRiskScore = accreditationFactor x overtoppingFactor x rehabFactor x warningBoost x egressBoost (scaled to 0-100) ``` - **accreditationFactor** — Non-Accredited scores highest, Provisionally Accredited (PAL) next, unclear/no-info text next, Accredited lowest. - **overtoppingFactor** — derived from the NLD's reported annual chance of overtopping; unknown/missing chance is treated as **elevated** risk, not neutral, because a levee with no data on file is not one you can verify. - **rehabFactor** — Active PL 84-99 rehab enrollment (a known, currently flagged deficiency) scores higher than Not Enrolled. - **warningBoost** — a documented flood warning system reduces the score; no/unknown warning system boosts it. - **egressBoost** — zero mapped evacuation routes boosts the score the most; 1 route is a smaller boost; 2+ routes add nothing. Missing data is deliberately treated as risk-elevating throughout — a levee the NLD has no accreditation, overtopping, or egress data for is exactly the kind of levee that hasn't been checked recently, which is itself a risk signal. **This is not a flood-zone lookup or engineering certification.** Confirm any insurance, lending, or life-safety decision against official FEMA flood maps and a licensed engineer. ### Data source **USACE National Levee Database**, "Leveed Areas" layer (ArcGIS FeatureServer, `geospatial.sec.usace.army.mil/dls/rest/services/NLD/Public/FeatureServer/16`). Keyless, official, and free — you pay only for the records this actor returns. ### Use as an MCP tool This actor is exposed via [mcp.apify.com](https://mcp.apify.com) — AI agents (Claude, Cursor, etc.) can call it as a levee-exposure verification tool. The input schema (lat/lon/label list, or states/counties) and field-level output descriptions are designed to be read and chained by agents: geocode an address upstream, screen it here, branch on `behind_levee` / `leveeRiskScore` downstream. ### FAQ **How do I check if my property is behind a levee?** Geocode the address to lat/lon (any geocoder), pass it in `assets`, and read `behind_levee` and `leveeRiskScore` on the row. **Is a FEMA flood-zone "X" (no insurance required) always safe if there's a levee?** No. FEMA can zone land as protected by a levee it separately rates **Non-Accredited** — the levee doesn't meet FEMA's engineering standard, but the flood-zone map hasn't been updated to reflect that. Check `fema_accreditation_rating` and `non_accredited_flag` directly; don't rely on flood-zone letter alone. **What does `leveeRiskScore: null` with `behind_levee: false` mean?** The point is not inside any NLD-mapped leveed area — no levee-dependent risk to score from this data source. It says nothing about general flood risk. **Why might a point I expect to be "behind a levee" come back `behind_levee: false`?** The NLD only maps levees the Corps has surveyed and digitized; some small, private, or unmapped levees aren't in the database yet. This is a screen against the federal registry, not a guarantee of ground truth. **Can I rank all the levees in my county?** Yes — leave `assets` empty, set `states: ["CO"]` and `counties: ["Prowers"]`, and the top `maxResults` leveed areas by `leveeRiskScore` come back ranked. **Why is `overtopping_ace` sometimes null even though the levee has data?** The NLD itself reports "Unknown" for many systems — that's parsed to `null` and is treated as an elevated-risk input in the score, not a data gap on our side. # Actor input Schema ## `assets` (type: `array`): List of asset locations to check against USACE leveed-area polygons. Each item is an object: {"lat": \, "lon": \, "label": ""}. One dataset row (one billable check) is produced per asset, even if it matches no levee. When this is non-empty the actor runs in assets mode and ignores states/counties. Leave empty and set states/counties instead to run in inventory mode. ## `states` (type: `array`): Only used when assets is empty. US states to search, as 2-letter postal codes (CO, TX) or full names (Colorado). Leave empty (with counties also empty) to search the whole national inventory, bounded by Max results. ## `counties` (type: `array`): Only used when assets is empty. County names to search (matched as a substring, e.g. "Gallatin"). Combine with states to narrow a multi-county state search. ## `maxResults` (type: `integer`): In assets mode: caps how many of your assets are screened (one billed row per asset). In inventory mode: how many top leveed areas to output, ranked by leveeRiskScore descending (each row is one billed result). Default 500. ## Actor input object example ```json { "assets": [ { "lat": 45.903, "lon": -111.53, "label": "Gallatin County, MT (Madison River levee)" }, { "lat": 38.075, "lon": -102.28, "label": "Granada, CO (Granada Right Levee)" } ], "maxResults": 500 } ``` # Actor output Schema ## `dataset` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "assets": [ { "lat": 45.903, "lon": -111.53, "label": "Gallatin County, MT (Madison River levee)" }, { "lat": 38.075, "lon": -102.28, "label": "Granada, CO (Granada Right Levee)" } ], "maxResults": 500 }; // Run the Actor and wait for it to finish const run = await client.actor("malonestar/usace-levee-flood-risk-screener").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "assets": [ { "lat": 45.903, "lon": -111.53, "label": "Gallatin County, MT (Madison River levee)", }, { "lat": 38.075, "lon": -102.28, "label": "Granada, CO (Granada Right Levee)", }, ], "maxResults": 500, } # Run the Actor and wait for it to finish run = client.actor("malonestar/usace-levee-flood-risk-screener").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "assets": [ { "lat": 45.903, "lon": -111.53, "label": "Gallatin County, MT (Madison River levee)" }, { "lat": 38.075, "lon": -102.28, "label": "Granada, CO (Granada Right Levee)" } ], "maxResults": 500 }' | apify call malonestar/usace-levee-flood-risk-screener --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=malonestar/usace-levee-flood-risk-screener", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Levee Flood Risk Screener — USACE National Levee Database", "description": "Check if a property sits behind a US levee and how much to trust it. Screens assets or a state/county inventory against USACE's National Levee Database with a composite risk score from FEMA accreditation, overtopping chance, rehab status, warning system and egress routes.", "version": "1.0", "x-build-id": "NngAwNuIJUrP5TlfL" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/malonestar~usace-levee-flood-risk-screener/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-malonestar-usace-levee-flood-risk-screener", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/malonestar~usace-levee-flood-risk-screener/runs": { "post": { "operationId": "runs-sync-malonestar-usace-levee-flood-risk-screener", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/malonestar~usace-levee-flood-risk-screener/run-sync": { "post": { "operationId": "run-sync-malonestar-usace-levee-flood-risk-screener", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "assets": { "title": "Assets to screen (assets mode)", "type": "array", "description": "List of asset locations to check against USACE leveed-area polygons. Each item is an object: {\"lat\": , \"lon\": , \"label\": \"\"}. One dataset row (one billable check) is produced per asset, even if it matches no levee. When this is non-empty the actor runs in assets mode and ignores states/counties. Leave empty and set states/counties instead to run in inventory mode." }, "states": { "title": "States (inventory mode)", "type": "array", "description": "Only used when assets is empty. US states to search, as 2-letter postal codes (CO, TX) or full names (Colorado). Leave empty (with counties also empty) to search the whole national inventory, bounded by Max results.", "items": { "type": "string" } }, "counties": { "title": "Counties (inventory mode)", "type": "array", "description": "Only used when assets is empty. County names to search (matched as a substring, e.g. \"Gallatin\"). Combine with states to narrow a multi-county state search.", "items": { "type": "string" } }, "maxResults": { "title": "Max results", "minimum": 1, "maximum": 5000, "type": "integer", "description": "In assets mode: caps how many of your assets are screened (one billed row per asset). In inventory mode: how many top leveed areas to output, ranked by leveeRiskScore descending (each row is one billed result). Default 500.", "default": 500 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```