COMPARATIVE ANALYSIS OF SARIMA AND XGBOOST MODELS FOR URBAN AIR QUALITY PREDICTION IN SRI LANKA

Abstract

The research employs a quantitative comparative design to forecast fine particulate matter (PM2.5) concentrations in Colombo, Sri Lanka, using particulate data from the U.S. Embassy and meteorological data (i.e., air temperature at 2 meters, relative humidity at 2 meters, and wind speed at 2 meters) from the NASA Open Data Portal. The dataset spans January 2018 to July 2024, with 80% of the data (January 2018 to September 2022) used for training and the remaining 20% for testing. Data preprocessing involved interpolation of missing values, normalization, and engineering of lagged variables such as 24-hour PM2.5 lags. Two predictive models were compared: Seasonal Autoregressive Integrated Moving Average (SARIMA), which captures seasonal patterns, and Extreme Gradient Boosting (XGBoost), which models non-linear relationships and complex feature interactions. Model performance was evaluated using root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared metrics. The SARIMA model, implemented with the “forecast” package in R, achieved an RMSE of 16.99, a MAPE of 20.35%, and an R-squared of 0.68, and demonstrated superior seasonality modeling with a lower Bayesian Information Criterion (BIC). The XGBoost model, trained with the “xgboost” package in R, leveraged advanced regularization and parallel processing to reduce prediction errors by 18-22%, excelled in forecasting extreme pollution events, and identified lagged PM2.5 values as the most influential predictors. Diagnostic analyses showed SARIMA’s effectiveness in seasonality and residual behavior modeling, while XGBoost excelled at capturing key predictors and nonlinear effects. The findings underscore the potential of advanced machine learning, especially XGBoost and ensemble methods, for accurate and timely air quality forecasting tailored to Sri Lanka’s climatic and emission context. The study

recommends integrating such models into national air quality systems to enable real- time forecasts and health alerts, expanding the air sensor network in high-risk urban

areas, and enforcing targeted pollution regulations. Future research should explore hybrid modeling using satellite and real-time traffic data, alongside explainable AI techniques, to enhance forecast accuracy and support data-driven environmental policy.