Investigation of Seismic Hazard Assessment using Machine Learning Techniques in a Seismically Active Region.

Thesis Abstract

Abstract
This thesis presents an in-depth investigation into the assessment of seismic hazard in a seismically active region through the application of machine learning techniques. The study focuses on utilizing advanced computational methods to enhance the accuracy and efficiency of seismic hazard assessment, with the aim of providing valuable insights for disaster preparedness and risk mitigation strategies. The research begins with a comprehensive review of existing literature on seismic hazard assessment, machine learning algorithms, and their applications in geophysics. The background of the study highlights the significance of understanding seismic hazards in seismically active regions and the limitations of traditional methods in predicting and mitigating seismic risks. The problem statement identifies the challenges faced in current seismic hazard assessment practices, including the complexity of geological data, uncertainties in seismic events prediction, and the need for more reliable and precise hazard assessments. The objectives of the study are outlined to address these challenges, focusing on developing machine learning models that can effectively predict and analyze seismic hazards. The methodology chapter details the research design, data collection methods, feature selection techniques, model development, and validation procedures. Machine learning algorithms such as neural networks, support vector machines, and decision trees are employed to analyze seismic data and predict potential hazard scenarios. The chapter also discusses the evaluation metrics used to assess the performance of the models and validate the results. The findings chapter presents a detailed analysis of the results obtained from applying machine learning techniques to seismic hazard assessment. The discussion covers the accuracy of the predictive models, the impact of different features on hazard prediction, and the comparison of machine learning approaches with traditional methods. The implications of the findings for seismic risk management and disaster preparedness are also discussed. In conclusion, this thesis provides valuable insights into the application of machine learning techniques for seismic hazard assessment in seismically active regions. The study demonstrates the potential of advanced computational methods to improve the accuracy and reliability of seismic risk prediction, offering new opportunities for enhancing disaster preparedness and resilience. The findings contribute to the existing body of knowledge in geophysics and highlight the importance of integrating machine learning approaches into seismic hazard assessment practices.