Spatiotemporal analysis of air pollution using advanced machine learning techniques

Abstract

This thesis presents a unified framework for spatiotemporal analysis of air pollu- tion using advanced machine learning to enable short-horizon, citylevel forecasting and operational decision support. A leakage-safe, multi-source dataset is curated for 20 cities across Bangladesh and China, integrating pollutant observations with spatiotemporal covariates (e.g., meteorological and contextual signals) to model ur- ban pollution dynamics under heterogeneous conditions. The forecasting task is formulated as multi-output time-series regression over PM2.5, PM10, NO2, SO2, and CO. To capture short-term fluctuations and longer temporal dependencies while exploiting cross-pollutant structure, a multitask CNN–LSTM architecture is de- veloped with a shared feature backbone and pollutant-specific prediction heads. Performance is benchmarked against classical machine-learning baselines (including Random Forest and XGBoost) under cityaware evaluation to assess both accuracy and robustness. To address regional data imbalance, a cross-country transfer learn- ing strategy is evaluated by leveraging representations learned from data-rich source cities to improve forecasting in data-scarce target cities. Forecast reliability is en- hanced via Monte Carlo Dropout to estimate predictive uncertainty, while SHAP and Integrated Gradients provide complementary explanations of feature influence and temporal attribution. Finally, an early-warning episode detection layer converts forecasts into event-oriented alerts and diagnostics to support practical monitoring workflows. Overall, the proposed pipeline delivers more accurate, uncertainty-aware, and interpretable multi-pollutant forecasts suitable for risk-sensitive air quality man- agement in heterogeneous urban environments.