Publication Date
Spring 2025
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Navrati Saxena
Second Advisor
William Andreopoulos
Third Advisor
Akshay Ravi
Keywords
Landslide Prediction, Machine Learning, Feature Selection, Reinforcement Learning, Time-Series.
Abstract
Landslides pose significant risks to human life, the community, and the environment, yet their prediction remains a complex and unexplored challenge. Existing prediction models often rely on surface measurements and satellite images, neglecting the critical role, in providing deeper insights into landslide analysis. The literature review highlights a lack of research in time series decomposition techniques, despite their potential to improve prediction accuracy. Similarly, feature selection methods that enhance model robustness and precision have not been adequately addressed. This study presents a novel approach to predicting landslide displacement by combining feature selection through reinforcement learning techniques with advanced time-series machine learning modeling techniques. Reinforcement learning is used to dynamically select impactful features, optimizing the input for prediction models. These insights guide the development of advanced and hybrid machine learning models trained and tested on comprehensive datasets, aiming to enhance prediction accuracy and efficiency. In addition, this research emphasizes the importance of weighted evaluation methodologies to prioritize essential data points, ensuring robust predictions. This work sets a new benchmark for predictive modeling and operational strategies in landslide monitoring by addressing key field gaps.
Recommended Citation
Ankem, Mohith Sai Venkat, "Landslide Prediction Using Time-Series Decomposition, Reinforcement Learning-based Feature Selection and ML Models" (2025). Master's Projects. 1476.
DOI: https://doi.org/10.31979/etd.txpc-8yhk
https://scholarworks.sjsu.edu/etd_projects/1476
