Assessment of Landslide Susceptibility Using Machine Learning Algorithms in a Mountainous Region

Thesis Abstract

Abstract
Landslides represent a significant natural hazard in mountainous regions, posing threats to lives, properties, and infrastructure. This thesis focuses on the assessment of landslide susceptibility using machine learning algorithms in a specific mountainous region. The utilization of machine learning techniques in landslide susceptibility mapping has gained traction due to its ability to handle complex relationships and patterns within datasets. The primary objective of this research is to develop a robust model that integrates various environmental factors to predict landslide susceptibility accurately. The study commences with a comprehensive introduction that provides the background of the research, the problem statement, research objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. This sets the stage for the subsequent chapters that delve deeper into the literature review, research methodology, discussion of findings, and conclusion. In the literature review chapter, ten key themes related to landslide susceptibility assessment, machine learning algorithms, and their applications in geoscience are critically analyzed. This exploration provides a theoretical foundation for the research and highlights gaps in existing knowledge that this study aims to address. The research methodology chapter outlines the systematic approach employed in this study, including data collection, preprocessing, feature selection, model development, and validation. Eight methodological components are discussed in detail to ensure transparency and reproducibility of the research process. Chapter four presents an in-depth discussion of the findings derived from applying machine learning algorithms to assess landslide susceptibility in the target mountainous region. The results are analyzed, interpreted, and compared with existing models to evaluate the predictive performance and effectiveness of the proposed approach. Finally, the thesis concludes with a summary of the key findings, implications of the research, and recommendations for future studies. The significance of incorporating machine learning algorithms in landslide susceptibility mapping is highlighted, along with the potential applications and benefits for risk assessment and mitigation strategies in mountainous regions. Overall, this research contributes to the field of geoscience by demonstrating the utility of machine learning algorithms in enhancing landslide susceptibility assessments. The findings serve as a valuable resource for policymakers, land use planners, and researchers working towards better understanding and managing landslide risks in mountainous areas.