Project Abstract

Abstract
Landslides pose a significant threat to human lives, infrastructure, and the environment in many regions worldwide. This research project focuses on the analysis of landslide susceptibility in a selected region using remote sensing and Geographic Information System (GIS) techniques. The study aims to enhance our understanding of the factors contributing to landslide occurrence and to develop a reliable model for landslide susceptibility mapping. The research begins with a comprehensive introduction providing background information on landslides and their impacts, emphasizing the importance of effective landslide susceptibility assessment. The problem statement highlights the current challenges faced in landslide prediction and prevention, motivating the need for advanced techniques such as remote sensing and GIS. The objectives of this study include identifying the key factors influencing landslide susceptibility, collecting and analyzing relevant data using remote sensing technologies, and developing a GIS-based model for landslide susceptibility mapping. The limitations of the study, such as data availability and accuracy, are acknowledged, along with the scope of the research, which focuses on a specific region but can be applied to similar areas. The significance of this research lies in its potential to provide valuable insights for land use planning, disaster risk reduction, and emergency response strategies in landslide-prone areas. The structure of the research is outlined to guide the reader through the subsequent chapters, including a detailed explanation of key terms and concepts related to landslide susceptibility assessment. Chapter two presents a comprehensive literature review covering ten key aspects of landslide susceptibility analysis, including previous studies, methodologies, and technologies used in similar research. This review serves as a foundation for the methodology chapter, guiding the selection of appropriate techniques and approaches for data collection and analysis. Chapter three details the research methodology, which includes eight key components such as data collection, preprocessing, feature selection, model development, validation, and interpretation. Remote sensing data, including satellite imagery and digital elevation models, are processed and integrated into GIS for spatial analysis and modeling. Chapter four presents the discussion of findings, highlighting the key factors influencing landslide susceptibility in the selected region. The GIS-based model developed in this study is evaluated for its accuracy and reliability in predicting landslide-prone areas. The results are interpreted, and recommendations are provided for future research and practical applications. In conclusion, this research project contributes to the field of landslide susceptibility analysis by integrating remote sensing and GIS technologies to enhance our understanding of landslide dynamics. The findings provide valuable insights for land use planning and disaster management strategies, with implications for reducing the risks associated with landslides in vulnerable regions. Overall, this study advances the capabilities of landslide susceptibility assessment and highlights the potential for remote sensing and GIS techniques to improve landslide risk mitigation efforts.

Project Overview