## 5 Conclusions, Perspectives, and Possible Future Works  
This study addresses a critical knowledge gap concerning the challenges posed by heatwaves in Thailand, particularly emphasizing the need for comparative analyses across urban, peri-urban, and rural settings. By integrating geospatial analysis, remote sensing, and terrestrial data, our innovative methodology meticulously maps heatwave patterns across three socio-economic regions: Bangkok (urban), Pathum Thani (peri-urban), and Saraburi (rural). The integration of satellite LST data with ground-based observations has provided a nuanced understanding of heatwave patterns, emphasizing the complex dynamics that influence urban planning and public health. The methodology introduced in this study is distinctive in its comprehensive approach to combining various data types and analytical techniques to improve the precision of heatwave predictions. Our approach enhances the understanding of the spatial and temporal dynamics of heatwaves, providing the most detailed collection of air temperature and satellite-derived heatwave data to date. We effectively utilize the RF model alongside a diverse array of both temporal and spatial predictors to refine LST predictions, aligning with and advancing existing research. Key variables, such as the Day of Year and minimum daily air temperature, have been identified as essential for accurately predicting daytime and nighttime LST. This robust, accurate, and reliable methodology not only advances current practices but also demonstrates the potential of machine learning techniques, particularly the MODIS-MOD11A1 and MODIS-MYD11A1 models, in environmental data analysis despite challenges such as data gaps and the need for ongoing model refinement. Our study shows that both air temperature and satellite-derived LST data reveal significant vulnerabilities to heatwaves in urban and peri-urban areas, particularly in places like peri-urban Pathum Thani and urban Bangkok, which suffer from prolonged heatwave durations. We find that these areas, especially downtown Bangkok and Northern Pathum Thani, experience increasingly frequent and severe heatwaves, predominantly at night. A unique aspect of our research is the demonstration that LST can reliably serve as a proxy for heatwave analysis due to its high spatial correlation with ground-based measurements. This capability is particularly valuable in regions without weather stations. Highlighting the urgent need for effective climate adaptation strategies, our study leverages satellite LST data to enhance our understanding and response to heatwave impacts in densely populated regions where traditional data sources are unavailable. Furthermore, the limitations posed by the data, including the misalignment of temporal scales between the air temperature and MODIS-derived land surface temperatures, as well as gaps in the observational records, were significant. Despite these challenges, the study employed the most accessible datasets available, illustrating the need for improved data collection methods to enhance the reliability of future predictions. Looking ahead, future research should focus on developing comprehensive and interdisciplinary strategies to mitigate the impacts of heatwaves. Building on the foundation laid by this study, further efforts could explore the integration of public health, urban planning, and community resilience frameworks to develop holistic solutions. Additionally, the employment of advanced ensemble methods, such as artificial neural networks (ANNs), gene expression programming (GEP), and gradient boosting models (GBMs), could enhance the accuracy and reliability of predictions. In conclusion, this comprehensive study not only corroborates previous findings but also introduces new insights into the spatial-temporal dynamics of heatwaves, thereby enriching the scientific dialogue on urban and peri-urban climate resilience.