Analysis of Landslide Susceptibility using GIS and Machine Learning Techniques

Thesis Abstract

Abstract
Landslides are a significant natural hazard that poses a threat to human lives, infrastructure, and the environment. In order to mitigate the risks associated with landslides, it is crucial to accurately assess the susceptibility of areas to potential landslides. This thesis presents an in-depth analysis of landslide susceptibility using Geographic Information Systems (GIS) and Machine Learning techniques. The study focuses on developing a comprehensive framework that integrates spatial data, terrain characteristics, and environmental factors to predict landslide susceptibility in a given region. The research begins with an introduction that provides background information on landslides and the importance of assessing susceptibility. The problem statement highlights the need for accurate and efficient methodologies to predict landslide occurrences. The objectives of the study are to develop a GIS-based model that can effectively assess landslide susceptibility, identify the limitations of existing methods, define the scope of the study, and outline the significance of the research findings. The structure of the thesis is presented to guide the reader through the subsequent chapters, and key terms are defined to ensure clarity and understanding. The literature review in Chapter Two explores existing research on landslide susceptibility assessment, GIS applications, and Machine Learning algorithms. Ten key themes are discussed, including spatial analysis techniques, landslide triggers, and the integration of environmental variables in predictive modeling. The review provides a comprehensive overview of the current state of knowledge in the field and identifies gaps that the present study aims to address. Chapter Three details the research methodology, outlining the data collection process, preprocessing steps, and the development of the GIS and Machine Learning models. Eight key components are presented, including the selection of study area, acquisition of spatial data, feature selection, model calibration, and validation procedures. The methodology is designed to ensure the accuracy and reliability of the landslide susceptibility predictions. In Chapter Four, the findings of the study are extensively discussed, including the performance evaluation of the GIS and Machine Learning models, the identification of high-risk areas, and the interpretation of the results. The analysis highlights the effectiveness of the integrated approach in predicting landslide susceptibility and provides valuable insights for land use planning and risk management strategies. Finally, Chapter Five presents the conclusions drawn from the study and summarizes the key findings and implications. The research contributes to the advancement of landslide susceptibility assessment by demonstrating the utility of GIS and Machine Learning techniques in predicting potential landslide occurrences. Recommendations for future research and practical applications are also provided to guide further investigations in this important field. In conclusion, this thesis offers a comprehensive analysis of landslide susceptibility using GIS and Machine Learning techniques, providing a valuable contribution to the body of knowledge on landslide risk assessment. The findings have significant implications for disaster management and land use planning, highlighting the importance of proactive measures to mitigate the impact of landslides on vulnerable communities and infrastructure.