Thesis Abstract

Abstract
This thesis presents a comprehensive study on the investigation of seismic hazard assessment using machine learning techniques in a seismically active region. The research aims to enhance the accuracy and efficiency of seismic hazard assessment by leveraging advanced machine learning algorithms. The study focuses on a seismically active region to analyze the seismic hazard and develop predictive models that can aid in disaster risk reduction and mitigation strategies. The introduction provides an overview of the significance of seismic hazard assessment and the challenges associated with traditional methods. The background of the study highlights the increasing frequency of seismic events in the target region and the need for more effective hazard assessment techniques. The problem statement emphasizes the limitations of current approaches in accurately predicting seismic hazards and the potential benefits of incorporating machine learning methods. The objectives of the study include developing machine learning models for seismic hazard assessment, evaluating their performance against traditional methods, and providing recommendations for future research and practical applications. The limitations of the study are acknowledged, including data availability constraints and potential biases in the predictive models. The scope of the study is defined in terms of the geographical area, data sources, and methodology employed. The literature review chapter explores existing research on seismic hazard assessment, machine learning applications in geophysics, and relevant studies on earthquake prediction and risk analysis. The research methodology chapter outlines the data collection process, feature selection techniques, model training and evaluation methods, and validation procedures. The chapter also discusses the criteria for selecting machine learning algorithms and the tools used for data analysis. The discussion of findings chapter presents the results of the machine learning models in predicting seismic hazards and compares them with traditional approaches. The analysis includes the evaluation of model performance metrics, feature importance rankings, and insights into the factors influencing seismic activity in the region. The implications of the findings for disaster management and urban planning are also discussed. The conclusion and summary chapter summarize the key findings of the study, highlighting the contributions to the field of geophysics and the potential applications of machine learning in seismic hazard assessment. The thesis concludes with recommendations for future research directions, including the integration of real-time data, ensemble modeling techniques, and interdisciplinary collaboration in geophysical studies. Overall, this thesis contributes to the advancement of seismic hazard assessment by demonstrating the effectiveness of machine learning techniques in predicting and mitigating seismic risks in seismically active regions. The findings have implications for disaster preparedness, infrastructure resilience, and public safety measures in areas prone to earthquakes.

Thesis Overview