Thesis Abstract

Abstract
Landslides pose a significant threat to communities residing in mountainous regions, leading to loss of lives, property damage, and disruption of infrastructure. The assessment of landslide susceptibility is crucial for effective risk management and mitigation strategies. This thesis investigates the application of machine learning techniques in assessing landslide susceptibility in a mountainous region. The study focuses on leveraging the power of machine learning algorithms to analyze various factors contributing to landslide occurrences and predict susceptible areas with high accuracy. The research methodology involves the collection of geospatial data, including topographic attributes, land cover types, soil properties, and historical landslide events. Machine learning models such as Random Forest, Support Vector Machine, and Logistic Regression are applied to develop landslide susceptibility maps based on these input variables. The performance of the models is evaluated using statistical measures such as sensitivity, specificity, and area under the curve. The literature review provides a comprehensive overview of previous studies on landslide susceptibility assessment, machine learning applications in geosciences, and the significance of incorporating various environmental factors in landslide modeling. The review highlights the advantages and limitations of machine learning techniques compared to traditional methods in landslide susceptibility analysis. The findings of the study reveal the effectiveness of machine learning algorithms in predicting landslide susceptibility in the mountainous region. The developed susceptibility maps exhibit high accuracy in identifying areas prone to landslides based on the input variables considered. The results also demonstrate the importance of integrating multiple environmental factors to enhance the predictive capability of the models. The discussion chapter delves into the implications of the research findings and discusses the practical applications of the developed landslide susceptibility maps in land use planning, disaster risk reduction, and emergency response strategies. The limitations of the study, such as data availability and model complexity, are also addressed, providing insights for future research directions. In conclusion, the assessment of landslide susceptibility using machine learning techniques offers a promising approach for enhancing landslide risk assessment and management in mountainous regions. The study contributes to the growing body of knowledge on geospatial analysis and highlights the potential of machine learning in addressing complex geohazard challenges. Recommendations for further research include the refinement of models, the incorporation of real-time data, and the validation of results through field investigations. Keywords Landslide susceptibility, Machine learning, Geospatial analysis, Risk assessment, Mountainous region, Geohazards.

Thesis Overview

The project titled "Assessment of Landslide Susceptibility Using Machine Learning Techniques in a Mountainous Region" aims to address the critical issue of landslide susceptibility in mountainous regions through the application of advanced machine learning techniques. Landslides pose significant threats to infrastructure, communities, and the environment in mountainous areas, making accurate assessment and prediction of landslide susceptibility crucial for effective risk management and mitigation strategies. By leveraging machine learning algorithms, this research seeks to enhance the accuracy and efficiency of landslide susceptibility mapping, contributing to improved disaster preparedness and response in mountainous regions. The research will begin with a comprehensive review of existing literature on landslide susceptibility assessment methods, machine learning techniques, and their applications in geoscience. This review will provide a solid foundation for understanding the current state of research in the field and identifying gaps that can be addressed through the proposed study. The methodology chapter will outline the specific steps involved in the assessment of landslide susceptibility using machine learning techniques. This will include data collection, preprocessing, feature selection, model development, validation, and performance evaluation. Various machine learning algorithms such as decision trees, random forests, support vector machines, and neural networks will be explored to determine the most effective approach for predicting landslide susceptibility in the study area. The findings chapter will present the results of the landslide susceptibility assessment, highlighting the accuracy and reliability of the machine learning models developed. Spatial maps showing areas of high, moderate, and low landslide susceptibility will be generated, providing valuable insights for land use planning, hazard mitigation, and disaster risk reduction efforts in the mountainous region under study. The discussion chapter will delve into the implications of the research findings, comparing them with existing methodologies and highlighting the strengths and limitations of the machine learning approach. Recommendations for future research and applications in other mountainous regions will also be provided, emphasizing the potential for scalability and transferability of the developed models. In conclusion, the research will demonstrate the effectiveness of machine learning techniques in assessing landslide susceptibility in mountainous regions, offering a valuable contribution to the field of geoscience and disaster management. By enhancing our understanding of landslide dynamics and risk factors, the study aims to support informed decision-making and proactive measures to mitigate the impact of landslides on vulnerable communities and ecosystems.