Exploring time series models for landslide prediction: a literature review

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发表日期：

2024年9月5日

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Kyrillos M. P. Ebrahim,
Ali Fares,
Nour Faris,
Tarek Zayed

摘要

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.