Thesis Abstract

Abstract
The increasing frequency and severity of geological hazards such as earthquakes, landslides, and volcanic eruptions pose significant threats to communities worldwide. Traditional methods of predicting these hazards have limitations in terms of accuracy and efficiency. In recent years, machine learning has emerged as a promising tool for improving the prediction of geological hazards by analyzing complex data patterns and identifying potential risk factors. This thesis explores the application of machine learning algorithms in predicting geological hazards and evaluates their effectiveness in enhancing hazard forecasting and mitigation strategies. Chapter One provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. The chapter sets the stage for the subsequent chapters by outlining the rationale for using machine learning in predicting geological hazards. Chapter Two presents a comprehensive literature review that examines existing studies on the application of machine learning in geological hazard prediction. The review covers various machine learning algorithms, data sources, and case studies to highlight the potential benefits and challenges associated with using these methods in hazard forecasting. Chapter Three details the research methodology employed in this study, including data collection, preprocessing, feature selection, model training, and evaluation. The chapter also discusses the selection criteria for machine learning algorithms and the metrics used to assess their performance in predicting geological hazards. Chapter Four presents a detailed discussion of the findings obtained from applying machine learning algorithms to predict geological hazards. The chapter analyzes the accuracy, sensitivity, specificity, and other performance metrics of the models developed and highlights the key factors influencing hazard prediction outcomes. Chapter Five offers a comprehensive conclusion and summary of the project thesis. The chapter summarizes the main findings, discusses the implications of the research, and provides recommendations for future studies in the field of machine learning for geological hazard prediction. Overall, this thesis contributes to the growing body of knowledge on the application of machine learning in predicting geological hazards. By leveraging advanced algorithms and data analytics techniques, this research aims to enhance the accuracy and reliability of hazard forecasting models, ultimately helping to reduce the impact of geological disasters on vulnerable communities.

Thesis Overview