Assessing the Impact of Land Use and Land Cover Changes on Land Surface Temperature Dynamics in the Coastal Region of Bangladesh: A Comprehensive Analysis Using Deep Learning Techniques AHP Integration

This study examines the relationship between land use and land cover (LULC) changes and land surface temperature (LST) dynamics in the climate-vulnerable coastal regions of Bangladesh from 1990 to 2020. Utilizing multi-temporal Landsat imagery and advanced deep learning techniques, particularly Temporal Convolutional Networks (TCN), the research achieved a classification accuracy of 73% in 2020, outperforming other models such as XGBoost (71%). The findings reveal that rapid urbanization and deforestation are the principal drivers of increasing LST, with urban centers such as Khulna and Chittagong experiencing a temperature rise of up to 2.5°C over the study period. An Analytic Hierarchy Process (AHP)-based prioritization of LULC transitions identified agricultural-to-urban (weighted impact: 82%) and vegetation-to-urban conversions as the most significant contributors to LST escalation, whereas forested and water-covered areas were associated with relatively lower temperature increases. Seasonal analysis indicates a more pronounced warming during summer, with rural areas showing a mitigated rise due to residual vegetation cover. The study further underscores the compounding effects of climate change, suggesting that continued LULC transformations without adaptive measures could intensify future heat stress. To mitigate the urban heat island effect, the study recommends the implementation of green infrastructure, enforcement of forest conservation, and the promotion of climate-sensitive urban planning. By integrating deep learning with multi-criteria decision analysis, this research contributes a robust methodological framework and empirical insights to support sustainable land management and climate adaptation in coastal regions.