School of Science Department of Mathematics 42 A Machine Learning Approach to Study the Relationship between Urban Morphology and Urban Heat Island Supervisor: FUNG Jimmy Chi Hung / MATH Student: WANG Yuanfu / SSCI Course: UROP1000, Summer The urban heat island (UHI) effect raises concern as global warming accelerates and extreme weather frequently appears. Previous research has revealed the relationship between the UHI effect and urban morphology, while how they are related remains uncertain. In this study, the UHI effect is measured by land surface temperature (LST) in the Greater Bay Area. Then, several models of LST in Hong Kong, including the linear model, deep neural network (DNN) model, random forest (RF) model, and support vector machine (SVM) model, are built based on urban morphology data, along with other geographical factors. All of the prediction results have high correlation coefficients to true values. They show similar geographic patterns to the measured LST, indicating that urban morphology data can be used to model the urban heat island effect. The flaws in dealing with the data and the immature algorithm are considered responsible for the errors, and several potential optimizations are proposed. A Machine Learning Approach to Study the Relationship between Urban Morphology and Urban Heat Island Supervisor: FUNG Jimmy Chi Hung / MATH Student: ZHANG Hanning / COSC Course: UROP1100, Fall Urban information is very important to many scientific research areas. However, most specific information is not easy to acquire directly. Therefore, methods of getting such information indirectly are sometimes needed through remote sensing, machine learning, reverse engineering, etc. In this study, we are trying to retrieve one of the most important building information(the physical dimension) using a machine-learning algorithm to recognize buildings in different high-resolution images acquired from advanced online maps. And the result of the study will help to provide more information for the study of urban modeling.