Thesis Abstract

Abstract
The utilization of machine learning techniques in predicting geological hazards has gained significant attention in recent years due to its potential to enhance early warning systems and improve disaster management strategies. This thesis explores the application of machine learning algorithms in predicting geological hazards, with a specific focus on earthquakes, landslides, and volcanic eruptions. The study aims to address the limitations of traditional hazard prediction methods by leveraging the capabilities of machine learning models to analyze large volumes of geospatial and temporal data. Chapter One provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The literature review in Chapter Two examines existing research on machine learning applications in geological hazard prediction, highlighting the strengths and limitations of various approaches. Chapter Three outlines the research methodology, including the data collection process, feature selection techniques, model training and evaluation methods, and validation strategies. The chapter also discusses the challenges and potential biases associated with using machine learning for geological hazard prediction. In Chapter Four, the findings of the study are presented and discussed in detail. The performance of different machine learning algorithms in predicting geological hazards is evaluated, and the factors influencing prediction accuracy are analyzed. The chapter also explores the interpretability of machine learning models in the context of geological hazard prediction. Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the study for disaster management practices, and suggesting avenues for future research. The study contributes to the growing body of knowledge on the application of machine learning in predicting geological hazards and underscores the importance of incorporating advanced technologies in disaster risk reduction efforts. Overall, this thesis provides valuable insights into the potential of machine learning to revolutionize the field of geological hazard prediction, offering new opportunities for improving preparedness and response strategies in the face of natural disasters.

Thesis Overview