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Introduction

Anomaly detection in water distribution networks is a critical area of research that aims to identify abnormal events or behaviors in the network that could potentially indicate leaks, bursts, or other issues that could affect the overall performance of the system.

Water distribution networks are complex systems that consist of pipes, valves, pumps, and reservoirs that work together to deliver clean water to consumers. However, these networks are prone to various anomalies such as leaks, bursts, and pressure surges, which can lead to significant water loss, decreased water quality, and even system failures. Detecting these anomalies early on is crucial for maintaining the efficiency and reliability of the network.

This thesis explores the various techniques and methodologies used for anomaly detection in water distribution networks. By understanding and implementing these methods, water utilities can proactively monitor and maintain their systems, ultimately improving the sustainability and resilience of their infrastructure.

Table of Contents

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of water distribution networks
2.2 Anomaly detection techniques
2.3 Statistical methods for anomaly detection
2.4 Machine learning approaches for anomaly detection
2.5 Data-driven anomaly detection
2.6 Sensor technologies for anomaly detection
2.7 Case studies on anomaly detection in water distribution networks
2.8 Challenges and future directions in anomaly detection
2.9 Summary of literature review

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Data preprocessing techniques
3.4 Feature selection and extraction
3.5 Model development
3.6 Model evaluation
3.7 Validation techniques
3.8 Ethical considerations

Chapter 4: Discussion of Findings
4.1 Analysis of results
4.2 Comparison of methodologies
4.3 Interpretation of findings
4.4 Implications for water utilities
4.5 Recommendations for future research
4.6 Limitations of the study

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Conclusion
5.3 Contributions to the field
5.4 Practical implications
5.5 Future research directions

Thesis Overview

Anomaly detection in water distribution networks is a critical research area that aims to proactively identify abnormal events or behaviors in the network that could signal potential issues. This thesis explores various techniques and methodologies used for anomaly detection in water distribution networks, including statistical methods, machine learning approaches, and data-driven techniques.

Chapter 1 provides an introduction to the topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Additionally, key terms are defined to ensure a clear understanding of the content.

Chapter 2 offers a comprehensive literature review on anomaly detection in water distribution networks, covering various techniques, methodologies, case studies, challenges, and future directions in the field.

Chapter 3 details the research methodology, including research design, data collection methods, preprocessing techniques, feature selection, model development, evaluation, validation, and ethical considerations.

Chapter 4 presents a thorough discussion of findings, analyzing results, comparing methodologies, interpreting findings, discussing implications, offering recommendations for future research, and acknowledging study limitations.

Chapter 5 concludes the thesis by summarizing key findings, restating the thesis’s significance, outlining practical implications, suggesting future research directions, and wrapping up the project’s overall contributions to the field. Ultimately, this thesis aims to contribute to the advancement of anomaly detection in water distribution networks, enhancing the sustainability and resilience of water infrastructure systems.

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