Exploring time series models for landslide prediction: a literature review

Abstract 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.