🌍 N.O.V.A. — Geospatial Ozone Predictor

An advanced atmospheric intelligence platform using NASA data (1994–2021), Stochastic Machine Learning, and Real-Time Telemetry.

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📌 Overview

N.O.V.A. (North American Ozone Visual Analytics) is a Mission Control–style atmospheric intelligence system designed to analyze, visualize, and predict stratospheric and tropospheric Ozone (O₃) concentrations across Western North America.

It transforms nearly 30 years of NASA atmospheric back-trajectory data into an interactive desktop dashboard combining:

• 📊 Historical Geospatial Visualization
• 🤖 Random Forest Machine Learning Prediction
• 🛰️ Real-Time ISS Telemetry Tracking

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✨ Core Features

Feature	Description
🌌 3D Geospatial Visualization	Interactive 3D atmospheric ozone distribution
📈 Trend Analysis	Historical line graph and seasonal analysis
🤖 AI Prediction Engine	50-tree Random Forest Regressor
🛰️ Live ISS Tracker	Real-time satellite telemetry via API
🎛️ Mission Control UI	Dark cyber-corporate themed interface

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🖼 UI Preview

1️⃣ Dashboard

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2️⃣ 3D Visualization

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3️⃣ Line Graph Analysis

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4️⃣ AI Prediction Engine

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5️⃣ Live Satellite Location (ISS Tracking)

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🧠 Data Science & AI Logic

📥 Independent Variables (Features)

• Pressure (hPa) — Represents altitude
• Latitude
• Longitude
• Month
• Year

📤 Dependent Variable (Target)

• Ozone Concentration (ppbv)

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🤖 Model Details

• Algorithm: Random Forest Regressor
• Trees: 50 (n_estimators=50)
• Train/Test Split: 80/20
• Typical R² Score: 0.85 – 0.94
• Most Influential Feature: Pressure (Altitude)

Random Forest was selected because atmospheric ozone behavior is non-linear and seasonal, making linear regression insufficient.

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🛰️ Real-Time Telemetry

The ISS tracking system:

• Fetches live coordinates from a public API
• Updates every 3 seconds
• Runs in a background thread
• Prevents UI freezing using multithreading

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🏗 Architecture

N.O.V.A. follows the Model–View–Controller (MVC) pattern:

Layer	Role
Model	Data processing & AI engine
View	Tkinter GUI interface
Controller	User-triggered simulation & visualization logic

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🛠 Installation & Setup

🔹 1. Clone Repository

git clone https://github.com/arshc0der/N.O.V.A-Geospatial-Ozone-Predictor.git
cd N.O.V.A-Geospatial-Ozone-Predictor

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🔹 2. Create Virtual Environment (Recommended)

python -m venv venv

Windows

venv\Scripts\activate

macOS / Linux

source venv/bin/activate

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🔹 3. Install Dependencies

pip install -r requirements.txt

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🔹 4. Ensure Dataset File Exists

Place this file in the root directory:

Receptor_western_NAmerica_ozone_obs_1994_2021_from900to300.csv

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🔹 5. Run Application

python app.py

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📦 requirements.txt

pandas
numpy
scikit-learn
matplotlib
seaborn
requests
tkintermapview

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🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Commit changes
4. Submit a Pull Request

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📜 License

Distributed under the MIT License. © 2026 Arsh

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🚀 Built using NASA atmospheric back-trajectory data.