Thesis Abstract

**Abstract
** Landslides are a significant geological hazard that poses a threat to human life, infrastructure, and the environment. The ability to accurately predict landslide susceptibility is crucial for effective hazard management and mitigation strategies. This research project focuses on the analysis of landslide susceptibility mapping using machine learning techniques. The study aims to develop a robust model that can predict areas prone to landslides based on various geospatial factors. Chapter One provides an introduction to the research topic, presents the background of the study, identifies the problem statement, outlines the objectives of the study, discusses the limitations and scope of the research, highlights the significance of the study, and presents the structure of the thesis along with the definition of key terms. Chapter Two is dedicated to a comprehensive literature review, which covers ten key items related to landslide susceptibility mapping, machine learning techniques, and previous studies in the field. The review provides a solid foundation for understanding the current state of research and identifying gaps that this study aims to address. Chapter Three details the research methodology employed in this study. It includes the data collection process, data preprocessing techniques, the selection of machine learning algorithms, model training and evaluation procedures, feature selection methods, and validation techniques. The chapter also discusses the study area and the dataset used for analysis. Chapter Four presents an in-depth discussion of the findings obtained from the analysis of landslide susceptibility mapping using machine learning techniques. The chapter highlights the performance of the developed model in predicting landslide-prone areas and compares it with existing models. The results are analyzed in detail, and the implications of the findings are discussed in the context of landslide hazard management. Chapter Five serves as the conclusion and summary of the project thesis. It provides a recap of the research objectives, methodologies, findings, and conclusions drawn from the study. The chapter also discusses the practical implications of the research findings, offers recommendations for future research, and concludes with the overall significance of the study in the field of geo-science and hazard management. In conclusion, this research project contributes to the field of geo-science by demonstrating the efficacy of machine learning techniques in landslide susceptibility mapping. The developed model provides a valuable tool for identifying high-risk areas prone to landslides, thereby aiding in the effective planning and implementation of mitigation strategies. The study underscores the importance of leveraging advanced technologies to enhance hazard management practices and improve the resilience of communities facing geological threats.

Thesis Overview

The project titled "Analysis of Landslide Susceptibility Mapping Using Machine Learning Techniques" aims to investigate and analyze the factors influencing landslide susceptibility through the application of machine learning techniques. Landslides are a significant natural hazard that poses risks to both human lives and infrastructure. Understanding the factors that contribute to landslide occurrence and developing accurate susceptibility maps are crucial for effective risk assessment and mitigation strategies. The research will focus on utilizing machine learning algorithms to analyze various geospatial data layers, such as topography, geology, land cover, rainfall intensity, and slope stability, to predict landslide susceptibility in a specific study area. By leveraging the power of machine learning, the project seeks to enhance the accuracy and efficiency of landslide susceptibility mapping compared to traditional methods. The study will involve collecting and preprocessing relevant geospatial data, including satellite imagery, digital elevation models, and geological maps. Machine learning algorithms, such as decision trees, random forests, support vector machines, and neural networks, will be applied to analyze the data and develop a predictive model for landslide susceptibility. The performance of the machine learning model will be evaluated using metrics such as accuracy, sensitivity, specificity, and area under the curve. The research overview will also include a detailed description of the study area, the methodology employed, and the significance of the project in the field of geoscience. The ultimate goal of the research is to provide valuable insights into landslide susceptibility assessment using machine learning techniques and contribute to the development of more accurate and reliable landslide risk management strategies.