Analysis of Landslide Susceptibility using Machine Learning Algorithms: A Case Study of a Region

Thesis Abstract

Abstract
Landslides are natural phenomena that pose significant threats to human lives, infrastructure, and the environment. Understanding and predicting landslide susceptibility is crucial for effective disaster management and risk reduction strategies. This thesis presents an in-depth analysis of landslide susceptibility using machine learning algorithms, focusing on a specific region as a case study. The study aims to explore the effectiveness of machine learning techniques in predicting landslide susceptibility and to provide valuable insights for landslide risk assessment and mitigation efforts. The research methodology involves the collection of geospatial data related to topography, geology, land use, and historical landslide occurrences in the study area. Various machine learning algorithms such as Random Forest, Support Vector Machine, and Artificial Neural Networks are applied to develop landslide susceptibility models. The performance of these models is evaluated based on metrics such as accuracy, sensitivity, specificity, and area under the curve. The findings of the study reveal that machine learning algorithms demonstrate promising results in predicting landslide susceptibility. The analysis identifies key factors contributing to landslide occurrence, including slope gradient, soil type, land cover, and proximity to roads and water bodies. The developed models exhibit high accuracy in delineating landslide-prone areas, providing valuable information for decision-makers and stakeholders involved in disaster risk management. The discussion of the findings highlights the significance of incorporating machine learning techniques in landslide susceptibility mapping and emphasizes the importance of considering spatial variability and uncertainty in landslide hazard assessment. The study contributes to the existing body of knowledge on landslide susceptibility modeling and presents a practical framework for integrating machine learning approaches into landslide risk analysis. In conclusion, this thesis underscores the potential of machine learning algorithms as effective tools for analyzing landslide susceptibility and enhancing landslide risk assessment strategies. The study emphasizes the importance of adopting a multidisciplinary approach that combines geospatial technologies, machine learning methods, and domain knowledge to improve the accuracy and reliability of landslide susceptibility models. The insights gained from this research can inform policy decisions, land use planning, and emergency preparedness measures to mitigate the impact of landslides and protect vulnerable communities in landslide-prone regions.