Thesis Abstract

Abstract
The rapid advancements in machine learning techniques have opened up new possibilities for applications in various fields, including the prediction of geological hazards. This thesis explores the use of machine learning algorithms in predicting geological hazards, focusing on their effectiveness and reliability compared to traditional methods. The study aims to address the limitations of existing approaches by leveraging the power of machine learning to improve the accuracy and efficiency of hazard prediction. Chapter 1 provides an introduction to the research topic, giving background information on the significance of predicting geological hazards and highlighting the problem statement. The objectives of the study are outlined, along with the limitations and scope of the research. The chapter concludes with the significance of the study and an overview of the thesis structure. Chapter 2 presents a comprehensive literature review covering ten key aspects related to machine learning and geological hazards prediction. This review delves into existing studies, methodologies, and technologies used in hazard prediction, highlighting the strengths and weaknesses of current approaches. Chapter 3 details the research methodology employed in this study, including data collection methods, feature selection, model training, and evaluation techniques. The chapter outlines the steps taken to implement machine learning algorithms for predicting geological hazards, ensuring the reliability and accuracy of the results. In Chapter 4, the findings of the research are discussed in detail, presenting the outcomes of the machine learning models applied to predict geological hazards. The chapter analyzes the performance metrics, compares the results with traditional methods, and discusses the practical implications of the findings. Chapter 5 concludes the thesis by summarizing the key findings and contributions of the study. The implications of the research are discussed, along with recommendations for future work in this field. The conclusion highlights the effectiveness of machine learning in predicting geological hazards and its potential for improving hazard management strategies. 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 techniques, the study demonstrates the potential for more accurate and reliable hazard prediction, leading to enhanced disaster preparedness and mitigation efforts.

Thesis Overview