Machine Learning for Environmental Noise Classification in Smart Cities / by Ali Othman Albaji.

Format:

Book

Author/Creator:

Albaji, Ali Othman.

Series:

Synthesis Lectures on Engineering, Science, and Technology, 2690-0327

Language:

English

Subjects (All):

Noise control.

Machine learning.

Environmental sciences--Social aspects.

Environmental sciences.

Urban policy.

Noise Control.

Machine Learning.

Environmental Social Sciences.

Urban Policy.

Local Subjects:

Noise Control.

Machine Learning.

Environmental Social Sciences.

Urban Policy.

Physical Description:

1 online resource (179 pages)

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2024.

Summary:

We present a Machine Learning (ML) approach to monitoring and classifying noise pollution. Both methods of monitoring and classification have been proven successful. MATLAB and Python code was generated to monitor all types of noise pollution from the collected data, while ML was trained to classify these data. ML algorithms showed promising performance in monitoring the different sound classes such as highways, railways, trains and birds, airports and many more. It is observed that all the data obtained by both methods can be used to control noise pollution levels and for data analytics. They can help decision making and policy making by stakeholders such as municipalities, housing authorities and urban planners in smart cities. The findings indicate that ML can be used effectively in monitoring and measurement. Improvements can be obtained by enhancing the data collection methods. The intention is to develop more ML platforms from which to construct a less noisy. The second objective of this study was to visualize and analyze the data of 18 types of noise pollution that have been collected from 16 different locations in Malaysia. All the collected data were stored in Tableau software. Through the use of both qualitative and quantitative measurements, the data collected for this project was then combined to create a noise map database that can help smart cities make informed decisions. In addition, this book: Machine learning-based sound classifier for environmental noise Qualitative analysis of community perceptions based on a noise pollution survey Create an interactive web dashboard and data warehousing for intelligent analytics reporting.

Contents:

List of Publications

Contents

Abbreviations

Symbols

List of Figures

List of Tables

1 Introduction

1.1 Overview

1.2 Problem Statement

1.3 Research Objectives

1.4 Scope of Project

1.5 Thesis Outline

References

2 Literature Review

2.1 Introduction

2.2 Research Background

2.3 Data Analytics and Data Visualization Dashboard

2.4 Machine Learning

2.4.1 Supervised Learning

2.4.2 Unsupervised Learning

2.5 Machine Learning Algorithms

2.5.1 Decision Tree (DT)

2.5.2 Logistic Regression (LR)

2.5.3 K-Nearest-Neighbor (KNN)

2.5.4 Support Vector Machine (SVM)

2.5.5 Random Forest (RF)

2.6 Machine Learning Parameters

2.6.1 Confusion Matrix

2.6.2 Classification Accuracy

2.6.3 Precision Generated by AI.

Notes:

Part of the metadata in this record was created by AI, based on the text of the resource.

Description based on publisher supplied metadata and other sources.

ISBN:

9783031546679

3031546679

OCLC:

1427972468