Machine Learning for Environmental Noise Classification in Smart Cities

Ali Othman Albaji received a bachelor's degree in electrical engineering specializing in "General communications" from the Civil Aviation Higher College, Tripoli, Libya, in 2007, and a Master's degree in electronics and telecommunication engineering from University Technology Malaysia *UTM*, Johor Bahru, Malaysia in 2022. His research interests are Machine Learning (ML), IoT, Wireless Sensor Networks (WSN), VSAT, SCADA Systems, Optical Networking, Wireless Communications, Deep Learning (DL), Artificial intelligence (AI), Web design, Robotics, and Programming Languages expert / Traineron ( Python, MATLAB, JAVA, JAVA Script, SQL, Data Base MSQL, C++, HTML, and....ETC).

CHAPTER 1 INTRODUCTION ... 1
1.1 Overview ... 1
1.2 Problem Statement ... 3
1.3 Research Objectives ... 4
1.4 Scope of Project ... 4
1.5 Thesis Outline ... 5

CHAPTER 2 LITERATURE REVIEW ... 7
2.1 Introduction ... 7
2.2 Research Background ... 9
2.3 Data analytics and data visualization dashboard ... 15
2.4 Machine Learning ... 15
2.4.1 Supervised learning ... 16
2.4.2 Unsupervised learning ... 17
2.5 Machine Learning Algorithms ... 17
2.5.1 Decision Tree (DT) ... 17
2.5.2 Logistic Regression (LR) ... 18
2.5.3 K-nearest-neighbor (KNN) ... 19
2.5.4 Support Vector Machine (SVM) ... 20
2.5.5 Random Forest (RF) ... 21
2.6 Machine Learning parameters ... 22
2.6.1 Confusion Matrix ... 22
2.6.2 Classification Accuracy ... 23
2.6.3 Precision ... 23
2.6.4 Recall ... 23
2.6.5 F1-Score ... 23
2.7 MATLAB software ... 24
2.8 Python software ... 24
2.9 Tableau software ... 25
2.10 Effects of Noise Pollution ... 25
2.10.1 Effects of Noise on Older Adults ... 26
2.11 Perceptions of Noise ... 26
2.12 Fundamentals of Noise ... 28
2.12.1 Individual Vehicles ... 28
2.12.2 Aircraft Noise ... 29
2.12.3 Wind-Turbine Noise ... 29
2.12.4 Mechanical Noise ... 30
2.12.5 Railway Noise ... 30
2.13 Environmental Noise Modeling and Monitoring ... 31
2.14 Conservation Program and Control Measures ... 34
2.15 Existing Apps for Noise Data Capturing ... 36
2.15.1 NoiseCapture App ... 36
2.15.2 Too Noise Pro ... 36
2.15.3 NoisePlatform ... 37
2.16 Weighting Filters in Noise Measurements ... 37
2.16.1 Frequency Weighting ... 38
2.16.2 Time Weighting ... 38
2.17 Previous Works ... 40
2.18 Summary ... 54

CHAPTER 3 RESEARCH METHODOLOGY ... 55
3.1 Introduction ... 55
3.2 Project Flowchart ... 55
3.3 Proposed Machine Learning Based Approach for Noise Classification ... 56
3.4 Qualitative Data ... 59
3.4.1 Qualitative analysis based on a survey using SPSS ... 60
3.5 Development of an Interactive Web Dashboard ... 62
3.6 Summary ... 62

CHAPTER 4 RESULTS AND DISCUSSION ... 63
4.1 Introduction ... 63
4.2 Machine Learning classification using MATLAB ... 63
4.2.1 Validation Receiver Operating Characteristic curve
(ROC Curve) ... 64
4.2.2 Parallel Coordinates ... 66
4.2.3 Validation Confusion Matrix ... 67
4.3 Machine Learning classifications using Python ... 68
4.3.1 Introduction ... 68
4.3.2 Data preparation ... 68
4.3.3 Data analysis ... 69
4.3.4 Noise samples captured over Malaysian cities ... 70
4.3.5 Classification Models ... 73
4.3.6 Confusion Matrix ... 77
4.3.7 Relation between cities and noise types ... 78
4.4 Benchmark between (MonitorNoises Vs Yaseen et.al [14]) ... 85
4.4.1 Performance Comparison with Benchmarking ... 86
4.4.2 MonitorNoises (2023) ... 88
4.4.3 Project Improvement ... 90
4.5 Data warehousing using Tableau software ... 93
4.6 A Survey based on Noise Pollution Monitoring ... 99
4.6.1 Introduction ... 99
4.6.2 Relationship Knowledge of Noise Pollution with
Demographic Variable ... 104
4.6.3 Perception of Respondents regarding Noise Pollution ... 105
4.6.4 Correlation Analysis ... 112
4.7 Chapter Summary ... 114

CHAPTER 5 CONCLUSION AND RECOMMENDATIONS ... 117
5.1 Introduction ... 117
5.2 Contributions to Knowledge (REWRITE) ... 118
5.3 Future Works ... 119

REFERENCES 121