Thesis Abstract

Abstract
Landslides pose a significant threat to communities living in mountainous regions, causing loss of life, damage to infrastructure, and disruption to ecosystems. The ability to predict landslide susceptibility is crucial for effective risk management and mitigation strategies. This research project focuses on the analysis of landslide susceptibility using machine learning algorithms in a mountainous region. The study begins with a comprehensive literature review to explore existing research on landslide susceptibility assessment methods, machine learning algorithms, and their applications in geoscience. The research methodology section outlines the data collection process, data preprocessing techniques, feature selection methods, and the implementation of machine learning models for landslide susceptibility analysis. The findings of the study are discussed in detail, including the evaluation of different machine learning algorithms such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks in predicting landslide susceptibility. The results highlight the strengths and limitations of each algorithm in accurately identifying areas at high risk of landslides. The conclusion summarizes the key findings of the study, emphasizing the importance of machine learning algorithms in enhancing landslide susceptibility analysis. The implications of the research findings for risk management and disaster preparedness in mountainous regions are discussed, along with recommendations for future research directions. Overall, this study contributes to the advancement of geoscience by providing a data-driven approach to assessing landslide susceptibility and improving disaster risk reduction efforts in mountainous areas.

Thesis Overview