Enhanced Urban Heat Island Modeling with Machine Learning and Regression Kriging in a Topographically Diverse Medium-Sized City

Urban Heat Islands (UHIs) pose significant environmental challenges, particularly in medium-sized cities with diverse topographies. This study enhances UHI modeling by integrating the Random Forest machine learning algorithm with regression kriging techniques. Focusing on Cluj-Napoca, we address the complexities of spatial temperature variability and improve UHI mapping accuracy. The Random Forest algorithm models air temperature variations, and regression kriging enhances spatial interpolation by integrating explanatory variables and residuals. The findings reveal that the Random Forest algorithm significantly improves temperature variability explanation and reduces prediction errors. Results highlight notable spatial trends, with high-temperature sensors concentrated in the central eastern part of Cluj-Napoca. Improved UHI modeling has substantial implications for urban planning and smart city initiatives. Accurate temperature mapping enables targeted mitigation strategies, such as green infrastructure, improved urban design, and strategic placement of cooling systems. These efforts enhance urban livability, reduce energy consumption, and mitigate heat wave effects. This study underscores the importance of integrating advanced machine learning techniques with traditional geostatistical methods to address complex environmental challenges. The methodology and findings are relevant across scientific disciplines, offering a framework for the future.