Alam, Md. AhasanulRahman, ShafinIslam, NaeemRahman, Md. ShoaiburHridoy, Md. Moniruzzaman2026-06-092026-06-0920262026ID 21201363ID 24141077ID 21101322ID 21201316http://hdl.handle.net/10361/28338Cataloged from PDF version of thesis.Includes bibliographical references (pages 75-77).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2026.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.77 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Air pollution forecastingSpatiotemporal analysisMulti-task learningTransfer learningExplainable AIArtificial intelligence.Air--Pollution--Forecasting.Machine learning.Spatial analysis (Statistics).Thesis