Thesis Abstract

Abstract
This thesis investigates the application of Geographic Information Systems (GIS) in analyzing landslide susceptibility in a specific region. Landslides are a natural hazard that poses significant risks to human lives, infrastructure, and the environment. The use of GIS technology offers a powerful tool for assessing landslide susceptibility by integrating various spatial data layers and analytical techniques. The specific region chosen for this study is characterized by complex geological formations and topographical features that make it prone to landslides. The research aims to develop a comprehensive understanding of the factors influencing landslide susceptibility and to create a predictive model using GIS tools. The introduction provides a background to the study, highlighting the importance of addressing landslide susceptibility in the selected region. The problem statement identifies the lack of comprehensive landslide susceptibility analysis and the potential risks posed by landslides in the area. The objectives of the study include assessing the spatial distribution of landslides, identifying contributing factors, and developing a GIS-based model for landslide susceptibility mapping. The limitations of the study, such as data availability and scale constraints, are also discussed. The scope of the study encompasses the collection and analysis of relevant spatial data, the development of a GIS model, and the interpretation of results in the context of landslide susceptibility. The significance of the study lies in its potential to provide valuable insights into landslide susceptibility assessment and risk mitigation strategies in the specific region. By integrating GIS technology with geospatial data, this research contributes to improving the understanding of landslide dynamics and enhancing preparedness and response measures. The structure of the thesis outlines the organization of chapters, including the literature review, research methodology, discussion of findings, and conclusion. The literature review chapter explores existing research on landslide susceptibility assessment, GIS applications in landslide studies, and relevant methodologies for analyzing spatial data. Key themes include the factors influencing landslide occurrence, GIS-based modeling techniques, and case studies of landslide susceptibility mapping in similar regions. The chapter provides a comprehensive overview of the current state of knowledge in the field and identifies gaps that this study aims to address. The research methodology chapter outlines the data collection process, including satellite imagery, digital elevation models, geological maps, and land cover data. Various GIS techniques, such as spatial analysis, terrain modeling, and multicriteria decision analysis, are employed to assess landslide susceptibility. The chapter details the methodology for generating landslide susceptibility maps and validates the model using historical landslide events and field observations. The discussion of findings chapter presents the results of the GIS analysis, including the spatial distribution of landslides, the identification of susceptibility factors, and the validation of the predictive model. The chapter discusses the relationship between landslide occurrences and topographical features, geological conditions, and land cover types. Furthermore, the implications of the findings for landslide risk management and land use planning are explored. The conclusion chapter summarizes the key findings of the study and discusses their implications for landslide susceptibility assessment in the specific region. Recommendations for future research and practical applications of the GIS model are provided. The thesis concludes with a reflection on the significance of the study and its contribution to advancing knowledge in the field of landslide susceptibility analysis using GIS technology. In conclusion, this thesis demonstrates the potential of GIS technology in analyzing landslide susceptibility and provides valuable insights for improving risk assessment and mitigation strategies in the selected region. Through the integration of geospatial data and analytical tools, the study contributes to enhancing our understanding of landslide dynamics and supporting informed decision-making for sustainable development and disaster resilience.

Thesis Overview