Beyond the Hype: Comparing Lightweight and Deep Learning Models for Air Quality Forecasting

TL;DR

This study compares lightweight additive models like Facebook Prophet and NeuralProphet with complex deep learning models for urban air quality forecasting, finding that simpler models can achieve high accuracy and better interpretability.

Contribution

It demonstrates that interpretable additive models can rival deep learning approaches in air quality prediction, emphasizing their practicality and effectiveness.

Findings

FBP outperforms NP, SARIMAX, LSTM, and LightGBM in accuracy.

Both models achieve R^2 above 0.94 for PM forecasts.

Lightweight models offer a good balance of accuracy and interpretability.

Abstract

Accurate forecasting of urban air pollution is essential for protecting public health and guiding mitigation policies. While Deep Learning (DL) and hybrid pipelines dominate recent research, their complexity and limited interpretability hinder operational use. This study investigates whether lightweight additive models -- Facebook Prophet (FBP) and NeuralProphet (NP) -- can deliver competitive forecasts for particulate matter (PM$_{2.5}$, PM$_{10}$) in Beijing, China. Using multi-year pollutant and meteorological data, we applied systematic feature selection (correlation, mutual information, mRMR), leakage-safe scaling, and chronological data splits. Both models were trained with pollutant and precursor regressors, with NP additionally leveraging lagged dependencies. For context, two machine learning baselines (LSTM, LightGBM) and one traditional statistical model (SARIMAX) were also implemented. Performance was evaluated on a 7-day holdout using MAE, RMSE, and $R^2$. Results show that FBP consistently outperformed NP, SARIMAX, and the learning-based baselines, achieving test $R^2$ above 0.94 for both pollutants. These findings demonstrate that interpretable additive models remain competitive with both traditional and complex approaches, offering a practical balance of accuracy, transparency, and ease of deployment.