🌀 PRISM-TC · VAYUVEGA

AI Cyclone Intelligence: satellite-image intensity classification with an honest, transparent forecast dashboard

Smart India Hackathon 2026 · Problem Statement SIH26070 · North Indian Ocean basin

🚀 Live Demo · 🧪 Run Locally · 🔍 Real vs Simulated · 👥 Team

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✨ What is this?

PRISM-TC (dashboard name: VAYUVEGA) looks at a satellite image of a tropical cyclone, runs it through a deep-learning model, and shows the result on an interactive dashboard: intensity class, confidence, class probabilities, a past track and a 24-hour outlook.

💡 Our rule: no fake confidence. Every value on screen carries a tag, MODEL (from the AI), SIMULATED (historical-analog / rule-based) or EST. (estimated from the predicted class), so nobody is ever misled about what the AI actually predicted.

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🎯 Features

	Feature	What it does
🛰️	AI intensity classification	EfficientNet-B0 wind-regression ensemble predicts the cyclone intensity class from an image
📊	Confidence + class probabilities	Shows how sure the model is, not just its top answer (with a note that confidence can run high)
🗺️	Live track map	Observed IBTrACS track, simulated 24 h outlook and current position on a Leaflet map
📈	Intensity outlook	Simulated max sustained wind for the next 24 hours
⚡	Rapid-intensification check	Rule-based environment check (SST, wind shear, humidity) with 850 hPa vorticity shown for context only
🎬	Judge Demo Mode	3 prepared scenarios that run without needing internet or a good example image on hand
🚫	OOD rejection	Invalid, non-cyclone documents are rejected instead of being force-classified
🧾	Data provenance	See the image source, storm ID and position behind every prediction
🧪	Backtest & Transparency pages	Model performance computed from the project's own files, nothing filled in by hand
🛰️	Satellite view	Live INSAT visualization

🎬 Judge Demo scenarios

1. Valid TCIR sample → AI classification
2. Invalid document → OOD rejection
3. Live satellite → INSAT visualization

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🔍 What's real and what's simulated

Output	Source	Tag
Intensity class	🤖 EfficientNet-B0 ensemble	MODEL
Confidence	🤖 EfficientNet-B0 ensemble	MODEL
Class probabilities	🤖 EfficientNet-B0 ensemble	MODEL
Wind	📏 Class range from the predicted category	EST.
Track outlook	📚 Copied from a similar historical storm (IBTrACS)	SIMULATED
Intensity (wind) outlook	📚 Historical-analog	SIMULATED
Pressure	📏 Rule-based	SIMULATED
Risk index	📏 Rule-based	SIMULATED
Rapid-intensification flag	📏 Rule-based	SIMULATED

📈 Model performance

• 53.7% exact-class match
• 93.1% within one class
• Measured on a 12-storm, 520-image, storm-wise held-out test set (the model never saw these storms in training)

🧮 Dataset at a glance

• 3,205 storm observations (every 3 h) · 75 distinct storms · North Indian Ocean, seasons 2003-2016
• Max wind range 15-145 kt (mean 40.9 kt)
• 8.5× class imbalance (Depression vs Extremely Severe). Always guessing Depression would score 48%, which is why we also report within-one-class accuracy.

🌊 Data in use

TCIR (train/test) · IBTrACS (labels) · MOSDAC / INSAT (Biparjoy validation + live view) · ERA5 (environment check)

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🗂️ Project structure

PRISM-TC-Frontend/
├── index.html · style.css · app.js     # 🎨 the dashboard
├── backend/
│   ├── server.py                       # 🌐 serves the dashboard AND the /api routes
│   ├── logic.py                        # 🧭 track / risk / class logic
│   ├── predict.py                      # 🤖 the AI model call (needs model.pth, NO normalization)
│   ├── model.pth                       # ⚠️ copy this in (final model from Drive → scripts/)
│   ├── data/                           # 🌪️ historical storm positions (IBTrACS)
│   ├── samples/                        # 🖼️ test images for the demo
│   ├── tests/smoke_test.py             # ✅ sanity checks
│   └── requirements.txt
├── CLAUDE.md                           # 📝 notes for Claude (read first if you use it)
└── _original_frontend_backup/          # 💾 original design files, untouched

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🧪 Run it locally

Steps written for Windows, run from the project folder.

1️⃣ Install dependencies

pip install -r backend/requirements.txt

2️⃣ Add the model 🧠 Copy model.pth into the backend/ folder.

3️⃣ Start the server

python backend/server.py

4️⃣ Open the dashboard 🌐 Go to http://localhost:8000, then either:

• ▶️ press Run Demo, or
• 🖼️ pick a sample image / upload your own and press Generate AI Prediction

🚦 Status indicator

The top-left engine card and the sidebar show ONLINE · model loaded when the real model is running. Without model.pth the app runs in DEMO MODE with clearly labelled placeholder predictions.

✅ Smoke tests

python backend/tests/smoke_test.py          # check the backend
python backend/tests/smoke_test.py --real   # check with the real model

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🌍 Live deployment

Deployed on Render: https://prism-tc.onrender.com

⏳ Free-tier instances can take a little while to wake up on the first visit.

🌐 Internet & offline behaviour

Map and chart libraries and fonts load from CDNs. With no internet, the page falls back to a simple track plot and chart, so the demo keeps working. 🙌

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🧰 Tech stack

• 🎨 Frontend: HTML, CSS, JavaScript, Leaflet + OpenStreetMap
• ⚙️ Backend: Python web server with /api routes
• 🤖 Model: EfficientNet-B0 (PyTorch, model.pth)
• 🌪️ Data: TCIR, IBTrACS, MOSDAC/INSAT, ERA5

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👥 Team VAYUVEGA

	Member
🌀	Binayak Mandal
🌀	Kaushikee Karmakar
🌀	Ankita Kundu
🌀	Anurag Tiwari
🌀	Loknath Acharya
🌀	Kripa Das

Built with 💙 for Smart India Hackathon 2026 (SIH26070).

🌀 Turning satellite pixels into cyclone insight, honestly. 🌀