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Air Quality Forecasting Using Diffusion Modeling and ML Algorithms

This project focuses on predicting PM2.5 concentrations using a combination of classical machine learning models, deep learning architectures (like LSTM), and an advanced Denoising Diffusion Probabilistic Model (DDPM). The goal is to build a cutting-edge forecasting system that leverages the strengths of generative modeling and multivariate time-series engineering.

Project Overview

  • Forecasting air pollution, especially PM2.5, is critical for public health, urban planning, and climate research.
  • This project builds a full-stack ML pipeline:
    • Preprocessing raw CSV sensor data
    • Engineering lag and rolling features
    • Training classical regressors (Random Forest, XGBoost)
    • Training a sequence-based LSTM
    • Designing and implementing a custom DDPM to model time series in a novel way
  • Visual comparisons and performance benchmarks reveal strengths and limitations across models.

Screenshots of Highlights

Highlights

  • Multivariate Time Series Forecasting
  • Custom Sequence Diffusion Model (DDPM)
  • ML Benchmarking (XGBoost, RF, LSTM)
  • Feature-Rich Lag Analysis
  • Clean Modular Codebase

Project Structure

air-quality-diffusion/
│
├── data/ # Preprocessed and scaled input datasets
├── models/ # Saved model weights (.pth, .json, .pkl)
├── notebooks/ # Core notebooks (see below)
├── src/ # Source code
│ └── diffusion_model.py # DDPM architecture and forward pass logic
│
├── requirements.txt # Python dependencies
└── README.md # Project documentation

Notebooks Overview

01_explore_clean_data.ipynb

  • Loads raw CSV files
  • Handles missing values, interpolation, and scaling
  • Concatenates multivariate air quality features across sensors
    Output: scaled_data.csv

02_feature_engineering.ipynb

  • Constructs:
    • Lag features (t-1 to t-n)
    • Rolling means and standard deviations
    • Day of week / time encodings
      Output: Feature-rich dataset for ML modeling

02_dimensionality_rduction.ipynb

  • Applies PCA to reduce dimensionality of multivariate feature space
  • Compresses high-dimensional sensor data into a lower-dimensional latent space for modeling
  • Visualizes variance explained by each principal component and selects optimal number of components
  • Prepares compressed input features for use in downstream models (e.g., LSTM, XGBoost, Diffusion) Output: Reduced-dimension dataset and PCA transformation object

03_lstm_model.ipynb

  • Prepares sequential windows for LSTM training
  • Trains an LSTM model for PM2.5 forecasting
  • Plots predictions and error curves
    Output: RMSE, R², and predicted vs true PM2.5 plots

04_diffusion_modeling.ipynb

  • Implements a custom Simple1DDiffusionModel
  • Trains the DDPM to denoise a latent 1D vector representation of PM2.5 sequences
  • Forecasts via iterative reverse sampling
  • Benchmarks model predictions against ground truth
    Output: DDPM performance metrics and forecast visualizations

05_baseline_models.ipynb

  • Trains Random Forest and XGBoost regressors on lag feature data
  • Compares:
    • Random Forest
    • XGBoost
    • LSTM
    • DDPM
      Output: Final RMSE, R², and model performance comparison

Tech Stack

  • Python 3.10+
  • PyTorch – Deep learning and DDPM modeling
  • XGBoost – Gradient boosting
  • Scikit-learn – Baseline metrics and preprocessing
  • Pandas / NumPy / Matplotlib – Data wrangling and visualization

How the Diffusion Model Works

  • The DDPM is inspired by generative models like DALL·E and Stable Diffusion but adapted for 1D regression.
  • Instead of generating images, it learns to reverse noise applied to PM2.5 time series.
  • Sampling starts with Gaussian noise and refines over 100+ steps using learned denoising logic.
  • The model forecasts future values by iteratively cleaning noisy sequences.

Future Improvements

  • Calibrate the diffusion noise schedule (beta_t)
  • Use a Transformer-based denoiser for better attention over time
  • Explore multi-step forecasting (not just one time step ahead)
  • Integrate weather and environmental features (e.g., temperature, humidity)
  • Deploy as a web app using Streamlit or Flask