Exploring time series models for landslide prediction: a literature review

Introduction: Landslides pose significant geological hazards, necessitating advanced prediction techniques to protect vulnerable populations. Research Gap: Reviewing landslide time series analysis predictions is found to be missing despite the availability of numerous reviews. Methodology: Therefore, this paper systematically reviews time series analysis in landslide prediction, focusing on physically based causative models, highlighting data preparation, model selection, optimizations, and evaluations. Key Findings: The review shows that deep learning, particularly the long-short-term memory (LSTM) model, outperforms traditional methods. However, the effectiveness of these models hinges on meticulous data preparation and model optimization. Significance: While the existing literature offers valuable insights, we identify key areas for future research, including the impact of data frequency and the integration of subsurface characteristics in prediction models.