Flash Flood Early Warning System

The Story

The stunning Arizona deserts, known for their dry riverbeds and scenic beauty, hide a deadly risk. During the summer monsoon, sudden torrential rains can trigger flash floods in these seemingly harmless dry zones — with little or no warning. Conventional alert systems relying on rain gauges or weather satellites often fail to deliver timely, location-specific warnings. By the time a danger alert is sent, it might already be too late. But we have a smarter way. With VideoDB RTStream, we can install real-time cameras near flood-prone areas and let AI continuously monitor the visuals. As soon as the AI detects signs of a flash flood — like a sudden surge of water through dry land — it can instantly send alerts, giving local authorities and tourists precious moments to act.

What You’ll Build

With VideoDB RTStream, you can build a two-layer monitoring system that:

Continuously monitors dry riverbeds and surrounding areas
Detects flash floods immediately upon occurrence
Identifies heavy rainfall as early warning signals
Alerts for people in distress requiring rescue
Sends real-time notifications to emergency services

Setup

Install Dependencies

pip install videodb

Connect to VideoDB

import videodb

api_key = "your_api_key"
conn = videodb.connect(api_key=api_key)
coll = conn.get_collection()

Implementation

Step 1: Connect to the Flood Monitoring Stream

rtsp_url = "rtsp://samples.rts.videodb.io:8554/floods"
flood_stream = coll.connect_rtstream(
    name="Flood Detection Stream",
    url=rtsp_url,
    store=True,
)

Step 2: Create Primary Index - Flash Flood Detection

Create the primary scene index focused on immediate flood detection with more frequent analysis:

flood_understanding = flood_stream.understand(
    segmentation={"type": "time", "window": "5s"},
    analyzers=[{
        "type": "vlm",
        "name": "flood_scene",
        "sampling": {"frame_count": 3},
        "config": {"prompt": "Monitor the dry riverbed and surrounding area. If moving water is detected across the land, identify it as a flash flood and describe the scene."},
    }],
    store=True,
)
flood_scene_output = flood_understanding.outputs.get("flood_scene")

flood_scene_index = flood_stream.index(
    source=flood_scene_output,
    name="Flash_Flood_Detection_Index",
    use_for=["semantic"],
)

The frame_count: 3 captures 3 frames every 5 seconds for detailed water movement analysis.

Step 3: Define the Flash Flood Event

Create the first-stage event:

# Flash Flood Event
flood_event_id = conn.create_event(
    event_prompt="Detect a flash flood - sudden water surge across the riverbed.",
    label="flash_flood",
)

Step 4: Attach the Flash Flood Alert

import asyncio

webhook_url = "https://your-webhook-url.com"
ws_wrapper = conn.connect_websocket()
ws = await ws_wrapper.connect()

flood_alert_id = flood_scene_index.create_alert(
    flood_event_id,
    callback_url=webhook_url,
    ws_connection_id=ws.connection_id,
)

Finish the First Monitoring Stage

Disable its alert, stop its index and source understanding, and close its WebSocket before starting the independent riverbed-monitoring lesson.

flood_scene_index.disable_alert(flood_alert_id)
flood_scene_index.stop()
flood_understanding.stop()
await ws_wrapper.close()

Step 5: Create the Riverbed Monitoring Index

This independent stage analyzes five-second windows with three frames for rainfall and rescue signals.

riverbed_monitoring_understanding = flood_stream.understand(
    segmentation={"type": "time", "window": "5s"},
    analyzers=[{
        "type": "vlm",
        "name": "riverbed_scene",
        "sampling": {"frame_count": 3},
        "config": {"prompt": "Monitor the dry riverbed and surrounding area. If any person is detected, output 'Person detected, rescue needed'. Else if rainfall is detected, output 'Rainfall detected'. Otherwise, output 'No significant events detected.'"},
    }],
    store=True,
)
riverbed_scene_output = riverbed_monitoring_understanding.outputs.get("riverbed_scene")

early_warning_index = flood_stream.index(
    source=riverbed_scene_output,
    name="Riverbed_Monitoring_Index",
    use_for=["semantic"],
)

Step 6: Attach Riverbed Alerts

rainfall_event_id = conn.create_event(
    event_prompt="Detect heavy rainfall - potential precursor to flooding.",
    label="heavy_rainfall",
)
rescue_event_id = conn.create_event(
    event_prompt="Detect a person in distress or stuck - rescue needed.",
    label="person_rescue_needed",
)

ws_wrapper = conn.connect_websocket()
ws = await ws_wrapper.connect()
rainfall_alert_id = early_warning_index.create_alert(
    rainfall_event_id,
    callback_url=webhook_url,
    ws_connection_id=ws.connection_id,
)
rescue_alert_id = early_warning_index.create_alert(
    rescue_event_id,
    callback_url=webhook_url,
    ws_connection_id=ws.connection_id,
)

Alert Payloads

Flash Flood Alert (Critical Priority)

When a flood is detected, the system sends an immediate alert with the exact location and video:

{
  "event_id": "event-flood-detection",
  "label": "flash_flood",
  "confidence": 0.95,
  "explanation": "Flash flood detected! Water is flowing rapidly and forcefully through the riverbed. The water appears muddy, carrying sediment and debris. Multiple people visible on rocky banks.",
  "timestamp": "2025-05-29T07:17:51.123456+00:00",
  "start_time": "2025-05-29T07:17:51.000000+05:30",
  "end_time": "2025-05-29T07:17:56.000000+05:30",
  "stream_url": "https://rt.stream.videodb.io/manifests/rts-019711a0-0fde-7911-b282-25bc0b4ecf65/1748475471000000-1748475476000000.m3u8"
}

Heavy Rainfall Alert (Warning Priority)

Early warning alerts help authorities respond proactively:

{
  "event_id": "event-rainfall-warning",
  "label": "heavy_rainfall",
  "confidence": 0.85,
  "explanation": "Heavy rainfall detected in the monitoring area. Conditions favorable for flash flooding.",
  "timestamp": "2025-05-29T07:10:00.000000+00:00",
  "start_time": "2025-05-29T07:10:00.000000+05:30",
  "end_time": "2025-05-29T07:10:15.000000+05:30",
  "stream_url": "https://rt.stream.videodb.io/manifests/rts-019711a0-0fde-7911-b282-25bc0b4ecf65/1748475000000000-1748475015000000.m3u8"
}

The Result

With this system in place, communities, tourists, and local authorities in Arizona’s desert regions can receive immediate alerts when a dangerous flash flood occurs — gaining critical seconds to take cover, clear routes, or initiate rescues. The two-layer system ensures:

Real-time flood detection for immediate response
Early rainfall warnings for proactive preparation
Rescue alerts for people in distress

Explore the Full Notebook

Open the complete implementation with advanced monitoring features and configuration options.

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Overview

Agentic Workflows

Video Search and Understanding

Live Intelligence

Content Factory

Programmatic Editing

Safety & Compliance

The Story

What You’ll Build

Setup

Install Dependencies

Connect to VideoDB

Implementation

Step 1: Connect to the Flood Monitoring Stream

Step 2: Create Primary Index - Flash Flood Detection

Step 3: Define the Flash Flood Event

Step 4: Attach the Flash Flood Alert

Finish the First Monitoring Stage

Step 5: Create the Riverbed Monitoring Index

Step 6: Attach Riverbed Alerts

Alert Payloads

Flash Flood Alert (Critical Priority)

Heavy Rainfall Alert (Warning Priority)

The Result

Explore the Full Notebook

Baby Crib Monitoring with AI

Property Intrusion Detection

​The Story

​What You’ll Build

​Setup

​Install Dependencies

​Connect to VideoDB

​Implementation

​Step 1: Connect to the Flood Monitoring Stream

​Step 2: Create Primary Index - Flash Flood Detection

​Step 3: Define the Flash Flood Event

​Step 4: Attach the Flash Flood Alert

​Finish the First Monitoring Stage

​Step 5: Create the Riverbed Monitoring Index

​Step 6: Attach Riverbed Alerts

​Alert Payloads

​Flash Flood Alert (Critical Priority)

​Heavy Rainfall Alert (Warning Priority)

​The Result

Explore the Full Notebook

​Related Tutorials

Baby Crib Monitoring with AI

Property Intrusion Detection

The Story

What You’ll Build

Setup

Install Dependencies

Connect to VideoDB

Implementation

Step 1: Connect to the Flood Monitoring Stream

Step 2: Create Primary Index - Flash Flood Detection

Step 3: Define the Flash Flood Event

Step 4: Attach the Flash Flood Alert

Finish the First Monitoring Stage

Step 5: Create the Riverbed Monitoring Index

Step 6: Attach Riverbed Alerts

Alert Payloads

Flash Flood Alert (Critical Priority)

Heavy Rainfall Alert (Warning Priority)

The Result

Related Tutorials