Deep Learning for High-Resolution Atmospheric Modeling & Virtual Sensing
Atmos AI is a specialized deep learning framework designed to bridge the gap between sparse physical sensor data and dense environmental insights. By leveraging Graph Neural Networks (GNNs), the project implements a "virtual sensor" architecture, capable of mapping air quality and atmospheric variables across complex spatial topologies without the need for exhaustive hardware deployment.
Traditional environmental monitoring is limited by the high cost of physical stations. Atmos AI treats geographical regions as dynamic graphs, where sensors are nodes and spatial relationships are edges. This allows the model to:
- Interpolate data in "blind spots" with high precision.
- Predict pollutant dispersion based on topological features.
- Scale air quality monitoring to entire cities at a fraction of the hardware cost.
This repository contains the primary AtmosModel engine. Key technical features include:
- Graph-Based Spatiotemporal Modeling: Uses GNNs to capture the non-linear relationships between neighboring sensor nodes.
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Feature Fusion: Designed to integrate diverse data streams including humidity, temperature, and particulate matter (
$PM_{2.5}$ /$PM_{10}$ ). - Virtual Sensor Inference: A custom-weighted logic that allows the model to act as a software-defined sensor for any given coordinate within the trained graph.
- Language: Python 3.10+
- Deep Learning: PyTorch / PyTorch Geometric
- Data Handling: NumPy, Pandas, Scikit-learn
- Mathematical Foundation: Graph Theory & Spatiotemporal Analysis
├── models/
│ └── atmos_gnn_model.py <-- The core architecture
└── README.md
Note: Ensure you have your dataset processed into a graph-compatible format (TUDataset or custom PyG Data objects).
import torch
from models.atmos_gnn_model import AtmosGNN
# Initialize the model
model = AtmosGNN(input_dim=12, hidden_dim=64, output_dim=1)
model.load_state_dict(torch.load('weights/atmos_v1.pt'))
model.eval()
# Generate virtual sensor data
with torch.no_grad():
prediction = model(graph_data)
print(f"Predicted Air Quality Index: {prediction}")- Integration of Satellite Imagery for multi-modal verification.
- Real-time API deployment for live environmental monitoring.
- Expansion into predictive forecasting (predicting AQI 24 hours in advance).