Thesis Abstract

Abstract
Landslides are a significant natural hazard that poses risks to human lives, infrastructure, and the environment, particularly in mountainous regions. Traditional methods of assessing landslide susceptibility often rely on manual interpretation of geological and topographical data, which can be time-consuming and subjective. In recent years, machine learning techniques have emerged as powerful tools for analyzing complex datasets and predicting landslide susceptibility with higher accuracy. This thesis presents an investigation into the application of machine learning techniques for assessing landslide susceptibility in a mountainous region. The study begins with a comprehensive review of existing literature on landslides, machine learning, and their intersection. The literature review highlights the limitations of traditional methods and the potential of machine learning algorithms to improve landslide susceptibility mapping. Various machine learning algorithms, including decision trees, support vector machines, and neural networks, are discussed in relation to their suitability for landslide susceptibility analysis. The research methodology section outlines the steps taken to collect and preprocess geospatial data, including topographic, geological, and land cover information. Feature selection techniques are employed to identify the most relevant variables for predicting landslide susceptibility. The machine learning models are trained and tested using historical landslide data to evaluate their performance and identify the most accurate algorithm for the study area. The findings of the study reveal the effectiveness of machine learning techniques in predicting landslide susceptibility in the mountainous region. The selected machine learning model demonstrates high accuracy in identifying areas at high risk of landslides, providing valuable insights for land use planning and disaster mitigation efforts. The discussion delves into the factors influencing landslide susceptibility, such as slope gradient, soil type, and land cover, and how these variables are captured by the machine learning model. In conclusion, this thesis contributes to the field of geoscience by showcasing the potential of machine learning techniques in assessing landslide susceptibility. The study highlights the importance of leveraging advanced technologies to enhance landslide risk assessment and improve disaster preparedness in mountainous regions. Recommendations for future research include exploring the integration of remote sensing data and real-time monitoring systems to further enhance landslide prediction accuracy. Keywords Landslide susceptibility, Machine learning, Geospatial analysis, Mountainous region, Disaster risk assessment. (Word count 291)

Thesis Overview

The research project titled "Investigation of landslide susceptibility using machine learning techniques in a mountainous region" aims to address the crucial issue of landslides in mountainous regions by leveraging the power of machine learning algorithms. Landslides pose significant threats to human life, infrastructure, and the environment, particularly in mountainous areas where the terrain is inherently unstable. Traditional methods of landslide susceptibility assessment have limitations in terms of accuracy and efficiency, highlighting the need for innovative approaches such as machine learning. This research project will focus on developing a predictive model that utilizes machine learning techniques to assess landslide susceptibility in a specific mountainous region. By analyzing various geospatial data such as terrain elevation, slope, soil type, land cover, and precipitation patterns, the model aims to identify key factors contributing to landslide occurrence. Through the integration of machine learning algorithms such as random forest, support vector machines, and neural networks, the model will be trained on historical landslide data to predict areas at high risk of future landslides. The research methodology will involve collecting and preprocessing geospatial data, selecting appropriate machine learning algorithms, training and validating the predictive model, and evaluating its performance in terms of accuracy and reliability. The project will also consider the interpretability of the model results to provide valuable insights for land use planning and disaster risk management in the mountainous region. By investigating landslide susceptibility using machine learning techniques, this research project aims to contribute to the advancement of landslide prediction and mitigation strategies in mountainous regions. The outcomes of this study are expected to enhance the understanding of landslide dynamics and provide decision-makers with valuable tools to mitigate the impact of landslides on communities and infrastructure. Ultimately, the findings of this research have the potential to inform sustainable land management practices and improve disaster resilience in mountainous areas vulnerable to landslide hazards.