School of Science Department of Mathematics 40 A Machine Learning Approach to Study the Relationship between Urban Morphology and Urban Heat Island Supervisor: FUNG Jimmy Chi Hung / MATH Student: CHEUNG Cheuk Lun Alan / MATH-AM Course: UROP2100, Fall Gas chromatography-mass spectrometry (GC-MS) is one of the most commonly used methods to analyze the trace of organic pollutants in the air. The sample gas is passed through the capillary column Gas chromatography, and different components in the sample gas are separated. Other components leave the capillary at different times (retention times). Together with mass spectra, GC-MS shows the various components inside the sample gas and their abundance. The main objective of this study is to develop computational pattern recognition algorithms for fast preliminary analysis for the identification of the components and their concentration in the sample gas. In this paper, an improved algorithm based on previous results will be introduced. Compared to the previous version, the decision tree method is added to work along with the known algorithm in identifying the peaks point of different components. Where the decision tree model divides the GC-MS graph into several parts, additional treatment is done for different parts to minimize errors. Also, some simpler but still efficient methods for finding peaks, starting points, and endpoints are introduced. A Machine Learning Approach to Study the Relationship between Urban Morphology and Urban Heat Island Supervisor: FUNG Jimmy Chi Hung / MATH Student: EE Hong Zhi / SENG Course: UROP1000, Summer The purpose of this project is to investigate the urban heat island phenomenon. Urban heat island is when temperatures in urban areas are higher relative to their unurbanized counterparts. This effect is seen in almost all heavily urbanized areas in the world. This project focuses on using machine learning techniques to confirm the positive correlation between urbanization and land surface temperature, specifically in the greater bay area. The data used in this project are land surface temperature, terrain, population density, and urban morphology data, the last of which is split into sub-datasets. After processing the data, it is used to train a deep neural network machine learning model, which was evaluated using mean absolute error as well as the correlation coefficient. The results support a positive correlation between urbanization and land surface temperature.
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