Analysis of Landslide Susceptibility Using GIS in a Mountainous Region

Thesis Abstract

Abstract
Landslides pose significant threats to communities residing in mountainous regions, leading to loss of lives, property, and disruption of socioeconomic activities. Geographic Information Systems (GIS) have proven to be valuable tools in assessing and mapping landslide susceptibility by integrating various spatial data layers. This thesis focuses on the analysis of landslide susceptibility using GIS techniques in a specific mountainous region. The research aims to identify areas at high risk of landslides, providing valuable information for land use planning and disaster risk management strategies. The study begins with a comprehensive introduction, providing background information on landslides, the significance of the research, and the scope of the study. The problem statement highlights the need for accurate landslide susceptibility mapping in mountainous regions to mitigate potential hazards effectively. The objectives of the study include developing a GIS-based model for landslide susceptibility assessment, identifying key factors influencing landslides, and evaluating the effectiveness of the model in predicting landslide occurrences. A thorough literature review in Chapter Two explores existing studies on landslide susceptibility mapping, GIS applications in landslide analysis, and relevant methodologies and techniques. The chapter synthesizes previous research findings to establish a solid foundation for the current study and identifies gaps that the research aims to address. Chapter Three outlines the research methodology, including data collection techniques, GIS tools and software used, and the process of developing the landslide susceptibility model. Key components of the methodology include the selection of landslide conditioning factors, data preprocessing, model development, and validation techniques. The chapter also discusses the limitations of the study and the measures taken to ensure the reliability and accuracy of the results. In Chapter Four, the findings of the study are presented and discussed in detail. The GIS-based model for landslide susceptibility assessment is applied to the study area, and the results are analyzed to identify high-risk zones. The chapter examines the influence of different factors on landslide occurrences and evaluates the performance of the model in predicting landslide susceptibility. Detailed maps and visualizations are included to illustrate the spatial distribution of landslide susceptibility in the mountainous region. Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the research, and providing recommendations for future studies. The significance of the study in enhancing landslide risk assessment and management practices in mountainous regions is highlighted, emphasizing the importance of integrating GIS technologies for effective decision-making and disaster preparedness. In conclusion, this thesis contributes to the field of geoscience by demonstrating the value of GIS in analyzing landslide susceptibility in mountainous regions. The research findings provide valuable insights for policymakers, urban planners, and emergency responders to implement proactive measures to reduce the impact of landslides on vulnerable communities.