Assessing Urban Heat Island (UHI) in Seberang Perai Tengah, Penang - Conventional Method vs Machine Learning - Bachelor of Surveying Science and Geomatics (Honours) Thesis

Aug 4, 2026

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Urban Heat Islands (UHI) are urbanized areas experiencing higher temperatures compared to rural regions due to human activities. Penang faces increasing UHI effects that threaten environmental sustainability and human well-being. This research maps and predicts UHI intensity across Penang using satellite imagery and a machine learning model, aiming to determine influential parameters, quantify their relationship with UHI intensity, and enable accurate prediction. Indices including NDVI, NDBI, NDWI, SMI, and LST support surface temperature analysis, with regression results for 2013 and 2023 and a Bagging model for spatial prediction.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages5

Reading Time13 min

AccessFree to download

Chapter 1 Introduction

Background of the Study

Problem Statement

Research Questions

Aim of the Study

Objectives of the Study

Scope and Limitation of Study

General Methodology

Software Used

Significance of Study

Organization of Chapter

Chapter 2 Literature Review

Introduction

Urban Heat Islands (UHI)

UHI Parameters

UHI Mapping and Analysis

Conventional methods for UHI detection

NURUL SYAHIRAH BINTI ZAINUDDIN BACHELOR OF SURVEYING SC IENCE AND GEOMATICS (HONOURS) JULY 2024 ASSESSING URBAN HEAT ISLAND (UHI) INTENSITY IN SEBERANG PERAI TENGAH, PENANG: CONVENTIONAL METHOD VS MACHINE LEARNING NURUL SYAHIRAH BINTI ZAINUDDIN 2022815298 SCHOOL OF GEOMATICS SCIENCE AND NATURAL RESOURCES COLLEGE OF BUILT ENVIRONMENT UNIVERSITI TEKNOLOGI MARA MALAYSIA JULY 2024 ASSESSING URBAN HEAT ISLAND (UHI) IN SEBERANG PERAI TENGAH, PENANG: CONVENTIONAL METHOD VS MACHINE LEARNING NURUL SYAHIRAH BINTI ZAINUDDIN 2022815298 Thesis submitted to the Universiti Teknologi MARA Malaysia in partial fulfilment for the award of the degree of the Bachelor of Surveying Science and Geomatics (Honours) DECLARATION I declare that the work on this project/dissertation was carried out in accordance with the regulations of Universiti Teknologi MARA (UiTM) . This project/dissertation is original and it is the result of my work, unless otherwise indicated or acknowledged as referenced work. In the event that my project/dissertation be found to violate the conditions mentioned above, I voluntarily waive the right of conferment of my degree of the Bachelor of Surveying Science and Geomatics (Honours) and agree be subjected to the disciplinary rules and regulations of Universiti Teknologi MARA. Name of Student Student’s ID No Project/Dissertation Title Signature and Date : Nurul Syahirah Binti Zainuddin: 2022815298 : Assessing Urban Heat Island (UHI) in Seberang Perai Tengah, Penang: Conventional Method vs Machine Learning : Approved by: I certify that I have examined the student’s work and found that they are in accordance with the rules and regulations of the School and University and fulfils the requirements for the award of the degree of Bachelor of Surveying Science and Geomatics (Honours) . Name of Supervisor : Dr Siti Nor Maizah Binti Saad Signature and Date : ABSTRACT Urban Heat Islands (UHI) are urbanized areas experiencing higher temperatures compared to rural regions due to human activities. Penang is facing increasing UHI effects, significantly impacting environmental sustainability and human well-being. This research focuses on mapping and predicting UHI intensity across Penang using satellite imagery and machine learning model. The objectives are to identify parameters influencing UHI intensity, quantify the relationship between these parameters and UHI, and utilize a machine learning model for UHI intensity prediction. Indices such as NDVI, NDBI, NDWI, SMI, and LST are used to analyze surface temperature variations. Regression analysis for 2013 shows a slight negative relationship between NDVI and LST (R-squared = 0.0115), indicating vegetation's minimal cooling effect. In 2023, the analysis reveals a moderate positive relationship between NDBI and LST (R-squared = 0.4611), suggesting built-up areas significantly increase surface temperatures. Additionally, a Bagging machine learning model predicts UHI intensity with high spatial accuracy. Results highlight the critical role of green spaces in mitigating UHI effects and the model's potential in urban planning and environmental management. Keywords: Urban Heat Islands (UHI), land surface temperature (LST), machine learning, regression analysis, environmental management TABLE OF CONTENTS CHAPTER TITLE PAGE CONFIRMATION BY PANEL OF EXAMINERS ii DECLARATION iii ABSTRACT iv ACKNOWLEDGEMENT v TABLE OF CONTENT vi LIST OF FIGURES ix LIST OF TABLES xi LIST OF ABBREVIATIONS xii 1 INTRODUCTION 1.1 Background of the Study 1 1.2 Problem Statement 4 1.3 Research Questions 5 1.4 Aim of the Study 5 1.5 Objectives ofthe Study 5 1.6 Scope and Limitation of Study 5 1.7 General Methodology 6 1.8 Software Used 7 1.9 Significance of Study 8 1.10 Organization of Chapter 8 2 LITERATURE REVIEW 2.1 Introduction 9 2.2 Urban Heat Islands (UHI) 9 2.3 UHI Parameters 10 2.4 UHI Mapping and Analysis 14 2.4.1

FAQs

What is the purpose of assessing UHI intensity in Seberang Perai Tengah, Penang?

The study aims to map and predict UHI intensity, identify parameters affecting it, quantify their relationship with UHI, and support prediction using a machine learning approach.

Which satellite-derived indices are used in the analysis?

Indices used include NDVI, NDBI, NDWI, SMI, and LST to analyze surface temperature variations and related UHI patterns.

How do the study results differ between 2013 and 2023?

For 2013, regression shows a slight negative relationship between NDVI and LST, indicating limited vegetation cooling. For 2023, the relationship between NDBI and LST is moderate and positive, suggesting built-up areas increase surface temperatures.

Aug 4, 2026

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