🚨 Disaster Alert System – HackNova

Team Name: HackNova
Team Members:

• Anish Bandal (Team Lead)
• Priti Chavan
• Swati Mane
• Atharva Gitaye

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📌 About the Project

The Disaster Alert System is an AI-powered real-time monitoring dashboard that detects, verifies, and tracks natural disasters from social media (Reddit).
It provides emergency teams with credible alerts, severity levels, and geolocation mapping for better disaster management.

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🔑 Key Features

• 🌐 Hybrid AI Pipeline

  • Custom ML models for sarcasm detection, binary disaster classification, and disaster type classification.
  • Gemini API fallback to re-check low-confidence predictions.

• 📍 Location Extraction & Mapping

  • NER + OpenStreetMap geocoding to pinpoint disaster locations.

• 🔥 Credibility & Severity Scoring

  • Multiple user posts increase credibility.
  • Auto-assigns severity levels (Low, Medium, High, Critical).

• 📊 Next.js Frontend Dashboard

  • Real-time map visualization & incident logs from Firebase Firestore.

• ⚡ Reddit Integration

  • Streams posts from disaster-related subreddits in real time.

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🤖 AI Models & Pipeline

Our AI system uses a multi-stage pipeline for accurate disaster detection:

1. Sarcasm Detection Model

   • Filters out sarcastic or ironic posts.
   • Example: "Wow, what an amazing earthquake 🙄" → Marked as sarcasm → Ignored.

2. Binary Disaster Classifier

   • Determines if a post actually describes a disaster or not.
   • Example: "Severe flooding in Pakistan displaces thousands" → Disaster: True.

3. Multiclass Disaster Classifier

   • Identifies which disaster it belongs to (Earthquake, Flood, Fire, Hurricane, etc. – 11 types).

4. Location Extraction (NER)

   • Uses Named Entity Recognition (NER) to detect locations in text.
   • Integrated with OpenStreetMap for latitude/longitude mapping.

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🔄 Hybrid Structure (Models + Gemini)

• If the confidence score from our custom ML models is high, we trust the prediction.
• If confidence is low or uncertain, the text is re-verified using Google Gemini API.
• The most reliable output is then stored in Firestore.
• This ensures high accuracy even when custom models are unsure.

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📊 System Architecture

flowchart TD
    R[Reddit API / Social Media] -->|New Post| B[Flask Backend]
    B -->|Sarcasm Model| S[Sarcasm Detection]
    S -->|Binary Classifier| D[Binary Disaster Classifier]
    D -->|Multiclass Disaster Model| M[Multiclass Disaster Model]
    M -->|NER + Geocoding| L[Location Extraction]
    L -->|Gemini Verification| G[Gemini Disaster Type Check]
    G -->|Confirmed Disaster| F[Firebase Firestore]
    F -->|Recent Incidents API| FE[Next.JS Frontend Dashboard]

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🛠️ Tech Stack

• Frontend: Next.js + Tailwind CSS
• Backend: Flask (Python)
• AI Models: Custom Transformers + Gemini API (hybrid)
• Database: Firebase Firestore
• Data Source: Reddit API (PRAW)

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🚀 How It Works

1. Reddit posts are streamed and passed to the backend.
2. Posts go through AI pipeline → Sarcasm filter → Binary classifier → Disaster type classifier.
3. Low-confidence results checked with Gemini.
4. Locations extracted and geocoded.
5. Events stored in Firestore with:
   • disaster_type, severity, credibility_score, reports_count, source_link, resolved.
6. Frontend dashboard fetches incidents and displays them on a map and analytics dashboard.

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📂 Project Status

✅ Frontend with real-time Firestore sync
✅ Backend with hybrid AI pipeline
✅ Reddit ingestion & Firestore storage
✅ Severity & credibility scoring
🚀 Fully integrated Emergency Dashboard