Thesis Abstract

Abstract
Landslides are natural hazards that pose significant risks to communities, infrastructure, and the environment. The ability to accurately predict landslide susceptibility is crucial for effective risk mitigation and disaster management. This thesis explores the application of machine learning techniques in landslide susceptibility mapping, aiming to enhance the accuracy and efficiency of predictive modeling. The study begins with an overview of the research background, highlighting the increasing importance of landslide susceptibility mapping in geoscience and the limitations of traditional methods. The problem statement emphasizes the need for more advanced and reliable techniques to improve the accuracy of landslide susceptibility assessments. The objectives of the study include developing a machine learning-based model for landslide susceptibility mapping, evaluating its performance, and comparing it with traditional methods. The research methodology section outlines the steps followed in data collection, preprocessing, feature selection, model training, and validation. Various machine learning algorithms are explored, including decision trees, random forests, support vector machines, and neural networks. The study evaluates the performance of these algorithms using metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. Chapter four presents a detailed discussion of the findings, highlighting the strengths and weaknesses of different machine learning algorithms in landslide susceptibility mapping. The results demonstrate the potential of machine learning techniques to improve the accuracy and efficiency of predictive models compared to traditional methods. The factors influencing landslide susceptibility are analyzed, providing valuable insights for future research and practical applications. The conclusion summarizes the key findings of the study and emphasizes the significance of applying machine learning in landslide susceptibility mapping. The thesis contributes to the existing body of knowledge by demonstrating the effectiveness of machine learning techniques in enhancing predictive modeling accuracy and efficiency. Recommendations for future research include exploring ensemble learning methods, incorporating more advanced features, and integrating remote sensing data for improved landslide susceptibility mapping. Overall, the study underscores the importance of adopting machine learning approaches in geoscience research, particularly in the field of landslide susceptibility mapping. By leveraging the power of data-driven modeling techniques, researchers and practitioners can enhance their ability to predict and mitigate landslide risks, ultimately contributing to the resilience of communities and infrastructure in landslide-prone areas.

Thesis Overview

The project, "Application of Machine Learning in Landslide Susceptibility Mapping," aims to harness the power of machine learning techniques to enhance the accuracy and efficiency of landslide susceptibility mapping. Landslides are natural hazards that pose significant risks to communities, infrastructure, and the environment. Traditional methods of landslide susceptibility mapping often rely on manual interpretation of geological, topographical, and environmental data, which can be time-consuming and subject to human biases. By incorporating machine learning algorithms such as artificial neural networks, support vector machines, and random forests, this research seeks to develop a more robust and automated approach to landslide susceptibility mapping. These algorithms can analyze large volumes of multidimensional data to identify complex patterns and relationships that may not be evident through traditional methods. By training these models on historical landslide data and relevant geospatial variables, the project aims to create predictive models that can accurately assess the likelihood of landslides occurring in specific areas. The research will involve collecting and preprocessing various data sources, including terrain elevation, rainfall patterns, soil properties, land cover, and past landslide occurrences. These data will be used to train and validate the machine learning models, which will then be applied to generate landslide susceptibility maps for the study area. The accuracy and performance of the machine learning models will be evaluated through statistical measures and validation techniques to ensure their reliability and effectiveness in predicting landslide susceptibility. The findings of this research are expected to provide valuable insights into the application of machine learning in landslide susceptibility mapping, offering a more efficient and accurate approach to assessing landslide risk. By automating the process of mapping landslide susceptibility, this project has the potential to enhance existing strategies for landslide hazard mitigation and land use planning. Ultimately, the successful implementation of machine learning techniques in landslide susceptibility mapping could contribute to better preparedness and resilience against landslide events, benefiting both communities and the environment.