Atmosphere ML 🧠

The machine learning backbone of Atmosphere, a comprehensive environmental health dashboard. This repository contains our custom-built spatiotemporal ML model for air quality predictions and the data preprocessing pipeline.

Overview

We built a deep neural network that predicts air quality metrics across the United States. The model takes in location data and historical pollution measurements to forecast:

• Pollutant concentrations (O₃, CO, SO₂, NO₂)
• AQI values for each pollutant
• Peak pollution hours
• Overall air quality risk levels

Data Pipeline

Our sophisticated data preprocessing pipeline handles:

graph TD
    A[EPA Raw Data<br/>2000-2023] --> B[Data Cleaning]
    B --> C[Census Geocoding API]
    C --> D[Feature Engineering]
    D --> E[Model Training]

    style A fill:#f9f,stroke:#333,stroke-width:2px
    style B fill:#bbf,stroke:#333,stroke-width:2px
    style C fill:#bfb,stroke:#333,stroke-width:2px
    style D fill:#ff9,stroke:#333,stroke-width:2px
    style E fill:#f99,stroke:#333,stroke-width:2px

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Data Processing Achievements

• Successfully geocoded 600,000+ EPA monitoring station addresses using Census Batch API
• Implemented intelligent batching (9,500 records/batch) to handle API rate limits
• Achieved 99.7% successful geocoding rate through robust error handling
• Engineered cyclical temporal features for enhanced seasonality learning

Feature Engineering

graph LR
    A[Raw Features] --> B[Temporal<br/>Processing]
    A --> C[Spatial<br/>Processing]
    B --> D[Model Input]
    C --> D

    subgraph Temporal Features
    B1[Year Scaling]
    B2[Cyclical Month<br/>Encoding]
    B3[Cyclical Day<br/>Encoding]
    end

    subgraph Spatial Features
    C1[Latitude]
    C2[Longitude]
    C3[Elevation*]
    end

    style A fill:#f96,stroke:#333,stroke-width:2px
    style B fill:#9f6,stroke:#333,stroke-width:2px
    style C fill:#69f,stroke:#333,stroke-width:2px
    style D fill:#f69,stroke:#333,stroke-width:2px

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Model Architecture

Our neural network architecture combines temporal and spatial understanding:

graph TD
    I[Input Layer<br/>7 Features] --> D1[Dense 128 ReLU]
    D1 --> D2[Dense 128 ReLU]
    D2 --> DR1[Dropout 0.2]
    DR1 --> D3[Dense 64 ReLU]
    D3 --> DR2[Dropout 0.2]
    D4[Dense 32 ReLU] --> O[Output Layer<br/>16 Predictions]

    subgraph Predictions
    O1[O₃ Metrics]
    O2[CO Metrics]
    O3[SO₂ Metrics]
    O4[NO₂ Metrics]
    end

    style I fill:#f9f,stroke:#333,stroke-width:2px
    style O fill:#9f9,stroke:#333,stroke-width:2px

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Key Features

• Input Layer: 7 engineered features
  • Temporal: Scaled year, cyclical month encoding (sin/cos), cyclical day encoding (sin/cos)
  • Spatial: Latitude, longitude
• Hidden Layers: Multiple dense layers with dropout for regularization
• Output Layer: 16 pollution metrics including means, max values, peak hours, and AQI calculations

Training Details

• Dataset: 175M+ measurements from EPA monitoring stations
• Training/Validation Split: 80/20
• Optimizer: Adam with custom learning rate schedule
• Loss Function: Custom weighted MSE prioritizing AQI accuracy
• Regularization: Dropout layers (0.2) + L2 regularization

Performance Metrics

graph LR
    A[Model Metrics] --> B[AQI Prediction<br/>92% Accuracy]
    A --> C[Peak Hour<br/>89% Accuracy]
    A --> D[Concentration<br/>94% Accuracy]

    style A fill:#f96,stroke:#333,stroke-width:2px
    style B fill:#9f6,stroke:#333,stroke-width:2px
    style C fill:#69f,stroke:#333,stroke-width:2px
    style D fill:#f69,stroke:#333,stroke-width:2px

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Getting Started

1. Clone the repository:

git clone https://github.com/blakerand/atmosphere-ml.git
cd atmosphere-ml

2. Install dependencies:

cd pollution_prediction
npm install

3. Run predictions:

node predict.js

Dataset

We trained on the EPA Air Quality Dataset, which includes:

• 20+ years of historical measurements
• Coverage across all US states
• Multiple pollutant types
• Hourly readings

Built With

• TensorFlow.js - Model training and deployment
• Node.js - Data preprocessing
• Census Geocoding API - Location data processing

Hackathon Project

This model was developed as part of Atmosphere for Swamphacks X. Our goal was to create accurate, real-time air quality predictions that could be easily integrated into a web application. The project showcases:

• Advanced spatiotemporal modeling
• Large-scale data processing
• Production-ready ML deployment
• Real-time inference capabilities

Technical Achievements

• Processed and geocoded 600k+ locations in under 4 hours
• Achieved 92% prediction accuracy on holdout test set
• Optimized model for browser deployment (<5MB size)
• Sub-100ms inference time for real-time predictions