Development of a GIS-based tool for landslide susceptibility mapping in a specific region.

Thesis Abstract

Abstract
This thesis focuses on the development of a Geographic Information System (GIS)-based tool for landslide susceptibility mapping in a specific region. Landslides are natural hazards that pose risks to human lives, infrastructure, and the environment. Mapping areas susceptible to landslides is crucial for effective disaster risk reduction and land use planning. The use of GIS technology allows for the integration of various spatial data layers to analyze and predict landslide susceptibility in a systematic and efficient manner. The study begins with an introduction that provides background information on landslides, their causes, and the importance of susceptibility mapping. The problem statement highlights the need for accurate and reliable tools to assess landslide susceptibility in the study area. The objectives of the study include developing a GIS-based tool, identifying factors contributing to landslide susceptibility, and evaluating the accuracy of the susceptibility map. The literature review in Chapter 2 explores existing methodologies and approaches for landslide susceptibility mapping, including statistical models, machine learning techniques, and hybrid models. Key factors influencing landslide susceptibility, such as slope gradient, lithology, land cover, and rainfall, are discussed in detail. The review also examines case studies of similar projects conducted in different regions around the world. Chapter 3 outlines the research methodology employed in this study, including data collection, processing, and analysis. The study area is defined, and relevant spatial data layers, such as digital elevation models, land cover maps, and geological maps, are obtained and integrated into the GIS platform. Various algorithms and models, such as Weight of Evidence and Analytical Hierarchy Process, are utilized to assess landslide susceptibility. Chapter 4 presents a detailed discussion of the findings obtained from the susceptibility mapping process. The GIS-based tool successfully identifies high, moderate, and low susceptibility zones within the study area based on the combination of contributing factors. The accuracy of the susceptibility map is evaluated using statistical measures and validation techniques, providing insights into the reliability of the model. In Chapter 5, the conclusion and summary of the project thesis are presented. The significance of the developed GIS-based tool for landslide susceptibility mapping is highlighted, emphasizing its potential applications in disaster risk management and land use planning. Recommendations for future research and improvements to the tool are also provided, aiming to enhance the accuracy and usability of the model. In conclusion, this thesis contributes to the field of surveying and geo-informatics by presenting a comprehensive approach to landslide susceptibility mapping using GIS technology. The developed tool offers a valuable resource for decision-makers, planners, and researchers to better understand and mitigate the risks associated with landslides in the specific region of study.