Thesis Abstract

Abstract
Landslides are a significant natural hazard that poses a threat to lives, infrastructure, and the environment, particularly in mountainous regions. Accurate mapping of landslide susceptibility is crucial for risk assessment and mitigation strategies. This thesis focuses on the application of machine learning algorithms for landslide susceptibility mapping in a mountainous region. The study area selected for this research is characterized by steep slopes, varied geological formations, and a history of landslides. The research begins with a comprehensive review of existing literature on landslide susceptibility mapping techniques, emphasizing the limitations of traditional methods and the potential benefits of machine learning approaches. The study aims to address the shortcomings of conventional methods by leveraging the predictive power and efficiency of machine learning algorithms in landslide susceptibility mapping. Chapter one provides an introduction to the research topic, outlining the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. The subsequent chapter delves into a detailed literature review, exploring ten key studies related to landslide susceptibility mapping, machine learning applications, and advancements in geospatial technology. Chapter three focuses on the research methodology, detailing the data collection process, preprocessing techniques, feature selection methods, and the implementation of machine learning algorithms for landslide susceptibility mapping. The methodology section also discusses model evaluation criteria, validation techniques, and the integration of remote sensing data for enhanced accuracy. Chapter four presents a comprehensive discussion of the research findings, including the performance evaluation of the machine learning models, the identification of significant variables influencing landslide susceptibility, and the comparison of results with traditional mapping approaches. The chapter also explores the implications of the findings for landslide risk management and the potential for operationalizing the developed models. Finally, chapter five offers a conclusion and summary of the thesis, highlighting the key findings, contributions to the field of geoscience, implications for future research, and recommendations for stakeholders involved in landslide risk assessment and mitigation. The abstract concludes with a reflection on the significance of utilizing machine learning algorithms for landslide susceptibility mapping in mountainous regions, emphasizing the potential for improving accuracy, efficiency, and decision-making in landslide risk management.

Thesis Overview

The research project titled "Utilizing Machine Learning Algorithms for Landslide Susceptibility Mapping in a Mountainous Region" aims to address the critical issue of landslide susceptibility in mountainous regions by leveraging advanced machine learning techniques. Landslides pose a significant threat to human lives, infrastructure, and the environment in hilly terrains due to the complex interactions of geological, hydrological, and environmental factors. Traditional landslide susceptibility mapping methods often rely on manual interpretation and are limited in their ability to capture the dynamic and intricate nature of landslide triggers and occurrences. This research project seeks to harness the power of machine learning algorithms to enhance the accuracy and efficiency of landslide susceptibility mapping in mountainous regions. By integrating various data sources such as topography, soil properties, land cover, rainfall patterns, and historical landslide records, machine learning models can analyze and identify patterns that contribute to landslide occurrences. The utilization of machine learning algorithms, including but not limited to decision trees, random forests, support vector machines, and neural networks, enables the development of predictive models that can forecast landslide susceptibility with greater precision. The research methodology involves collecting and preprocessing spatial data from the study area, including terrain elevation data, soil characteristics, land use/land cover information, and precipitation records. These datasets will be used to train and validate machine learning models to predict landslide susceptibility zones based on historical landslide events and environmental variables. The performance of the models will be evaluated using metrics such as accuracy, sensitivity, specificity, and area under the curve to assess their effectiveness in identifying areas at high risk of landslides. Through the comprehensive analysis of the research findings, this project aims to provide valuable insights into the application of machine learning algorithms for landslide susceptibility mapping in mountainous regions. The discussion of the results will highlight the strengths and limitations of different machine learning approaches, identify key factors influencing landslide susceptibility, and propose recommendations for improving landslide risk assessment and mitigation strategies in similar geographical settings. Overall, this research endeavor contributes to the advancement of geospatial technology and disaster management practices by introducing innovative methodologies for landslide susceptibility mapping. By harnessing the capabilities of machine learning algorithms, this project endeavors to enhance the accuracy, efficiency, and reliability of landslide risk assessments in mountainous regions, ultimately aiding in the development of proactive measures to mitigate the impact of landslides on vulnerable communities and ecosystems.