From Early Signal to Last-Mile Warning
Himalaya Shield is a functional hackathon prototype designed to demonstrate an end-to-end response system for Glacial Lake Outburst Floods (GLOFs) and sudden Himalayan flood hazards.
The system combines telemetry analysis, machine learning, calibrated risk scoring, generative AI, geospatial visualization, cross-border alert coordination, evacuation planning, sensor communication monitoring, and last-mile emergency dispatch into a single platform.
Glacial Lake Outburst Floods can develop rapidly and send destructive flood waves downstream.
The challenge is not only detecting an abnormal signal.
The real challenge is:
Detect → Assess → Coordinate → Evacuate → Alert
Mountainous terrain, limited warning time, communication failures, and geographically connected downstream communities make disaster response difficult.
A warning that reaches authorities but does not reach the community is not enough.
Himalaya Shield connects the entire emergency-response chain:
Historical / Telemetry Data
↓
Anomaly Detection
Isolation Forest
↓
Calibrated Risk Engine
↓
NORMAL / WARNING / CRITICAL
↓
Featherless AI
Emergency Intelligence
↓
Cross-Border Coordination
↓
Evacuation Planning
↓
SMS / Siren / Radio / Control Room
The goal is to convert abnormal environmental signals into explainable, actionable emergency intelligence.
🧠 AI/ML Architecture
1. Anomaly Detection
The system uses Isolation Forest to identify unusual telemetry behaviour.
The ML model acts as a supporting anomaly signal rather than independently triggering an emergency.
This helps reduce false positives when working with sparse or heterogeneous historical data.
2. Calibrated Hybrid Risk Engine
The final risk score combines multiple signals:
Signal Weight
Rate of water-level rise 40%
Historical deviation 25%
Persistence of abnormal trend 15%
ML anomaly signal 20%
The resulting score is classified into:
NORMAL
↓
WARNING
↓
CRITICAL
Example
A prototype telemetry event with:
Water-level change: 14.0 m
Risk score: 90/100
Risk level: CRITICAL
can trigger the emergency-response workflow.
🤖 Featherless AI Emergency Intelligence
Featherless AI is integrated as an AI decision-support layer after numerical risk calculation.
Risk Engine
↓
Risk Score + Risk Level
↓
Featherless AI
↓
┌─────────────────────┐
│ Assessment │
│ Immediate Action │
│ Community Message │
└─────────────────────┘
Important Design Principle
Featherless AI does not determine the numerical risk.
The calibrated backend risk engine remains responsible for calculating the risk.
The AI converts the result into concise human-readable emergency intelligence.
This keeps the system explainable and controlled.
📡 Sensor Communication Health
A disaster-monitoring system must also know when its sensors stop communicating.
The prototype monitors three communication states:
🟢 ONLINE
Telemetry communication is operational.
🟡 INTERMITTENT
Telemetry connection is unstable.
🔴 SILENT
The station has stopped transmitting data.
A silent station can generate a critical communication alert because missing data itself can become an operational risk.
Communication-health states are prototype-simulated and are not live sensor connectivity.
🏔️ Himalayan Monitoring Concept
The prototype represents a Himalayan monitoring corridor containing:
Imja Tsho
Tsho Rolpa
Everest Glacier Corridor
Rongbuk Glacier
Bhote Koshi Corridor
Arun River Corridor
These locations demonstrate how glacial lakes, glacier zones, and downstream river corridors can be represented in a unified monitoring system.
These are prototype monitoring locations and do not represent currently connected live sensors.
🌊 Cross-Border Emergency Coordination
Flood hazards can affect downstream regions beyond the location where the initial event occurs.
Himalaya Shield demonstrates a cross-border alert chain:
UPSTREAM DETECTION
↓
REGIONAL ALERT
↓
LOCAL RESPONSE
↓
COMMUNITY ALERT
Example:
Imja Tsho
↓
Nepal–Tibet Corridor
↓
Khumbu Valley
↓
Downstream Villages
The purpose is to demonstrate how early hazard information can be transformed into coordinated downstream response.
🚨 Evacuation Planning
The dashboard contains prototype evacuation zones with:
Hazard severity
Population exposure
Recommended action
Safe location
Evacuation route
Example:
Hazard Zone
↓
Risk Assessment
↓
Evacuation Route
↓
Safe Zone
Prototype response levels include:
EVACUATE
PREPARE
MONITOR
📢 Last-Mile Emergency Dispatch
The final stage is getting the warning to people.
The prototype demonstrates multiple communication channels:
📱 SMS
🚨 Siren
📻 Radio
🖥️ Control Room
👥 Emergency Response Teams
This creates a complete pipeline:
Detection
↓
Risk Assessment
↓
Emergency Intelligence
↓
Evacuation Decision
↓
Last-Mile Warning
📊 Real Historical Data
The prototype uses the:
CWC River Water Level Telemetry Hourly dataset
Dataset period:
1961–1990
The dataset contains historical telemetry records from multiple river monitoring stations.
It is used to demonstrate the anomaly-detection and risk-analysis pipeline.
Data Integrity
The dataset is historical real-world telemetry data.
It is not live 2026 Himalayan telemetry.
The Himalayan monitoring and communication components are currently represented as a functional prototype.
🖥️ Dashboard
The React dashboard provides a centralized emergency command interface.
It includes:
Himalayan monitoring sites
Water-level monitoring
Risk levels
ML anomaly status
Sensor communication health
Active alerts
Evacuation zones
Cross-border coordination
AI emergency intelligence
Emergency dispatch
GLOF simulation
Community reports
🎬 Full Emergency Demonstration
The system includes a complete demonstration workflow:
01 — GLOF Detection
↓
02 — Regional Coordination
↓
03 — Cross-Border Alert
↓
04 — Emergency Dispatch
↓
05 — Risk Analysis + Featherless AI
↓
06 — Response Complete
This demonstrates the complete journey from hazard detection to community warning.
🛠️ Technology Stack
Frontend
React
Vite
React Leaflet
JavaScript
CSS
Backend
Python
FastAPI
Pydantic
Machine Learning
Scikit-learn
Isolation Forest
Calibrated Hybrid Risk Engine
Generative AI
Featherless AI
OpenAI-compatible API
Data
CWC River Water Level Telemetry
Historical telemetry dataset
🏗️ Project Structure
GLOF-early-warning-System/
│
├── backend/
│ ├── data/
│ │ └── rwl_tele_hr_cwc_003_1961_1990.csv
│ │
│ ├── anomaly_detector.py
│ ├── cross_border.py
│ ├── featherless_ai.py
│ ├── main.py
│ ├── models.py
│ ├── telemetry.py
│ └── test_featherless_ai.py
│
├── frontend/
│ ├── src/
│ │ ├── App.jsx
│ │ ├── App.css
│ │ ├── SensorHealth.jsx
│ │ └── ...
│ ├── package.json
│ └── vite.config.js
│
└── .gitignore
⚙️ Running the Project
Backend
Navigate to the backend:
cd backend
Activate the Python virtual environment:
Windows PowerShell
.\venv\Scripts\activate
Start FastAPI:
uvicorn main:app --reload
Backend:
http://127.0.0.1:8000
Frontend
Open another terminal:
cd frontend
npm install
npm run dev
Frontend:
http://localhost:5173
🔐 Environment Variables
Featherless AI credentials must remain in a local .env file.
Example:
FEATHERLESS_API_KEY=your_api_key_here
FEATHERLESS_MODEL=Qwen/Qwen2.5-7B-Instruct
Never commit API keys or secrets to GitHub.
The repository .gitignore excludes environment files and virtual environments.
🎯 Current Prototype vs Future Deployment
✅ Currently Implemented
CWC historical telemetry integration
Isolation Forest anomaly detection
Calibrated hybrid risk engine
NORMAL / WARNING / CRITICAL classification
Sensor communication-health monitoring
Silent-station detection
Himalayan monitoring visualization
GLOF simulation
Cross-border alert workflow
Evacuation zones and routes
Last-mile dispatch simulation
Featherless AI integration
Emergency intelligence generation
Interactive React dashboard
🔬 Future Development
The prototype can be extended with:
Real-time Himalayan sensor networks
Satellite remote sensing
Real-time weather and rainfall feeds
DEM-based flood propagation modelling
Real-time river gauges
Glacier/lake change detection
Government emergency APIs
Multilingual SMS and voice alerts
Offline communication networks
Mobile emergency applications
Historical GLOF event model training
🌍 Impact
Himalaya Shield is designed around one core principle:
Early detection is valuable only when it leads to timely action.
The platform connects:
Sensing → Intelligence → Coordination → Evacuation → Communication
into a single disaster-response workflow.
🚀 Vision
Build a scalable Himalayan disaster-intelligence network that can transform fragmented environmental signals into fast, explainable and actionable warnings for communities at risk.
🏔️ HIMALAYA SHIELD
Detect Earlier. Decide Smarter. Warn Faster.
We are not just detecting a flood.
We are connecting detection to action.