Preprints
https://doi.org/10.5194/egusphere-2024-4160
https://doi.org/10.5194/egusphere-2024-4160
24 Jan 2025
 | 24 Jan 2025
Status: this preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).

Review Article: Rainfall‑Induced Landslide Prediction Models, Part I: Empirical-Statistical and Physically Based Causative Thresholds 

Kyrillos M. P. Ebrahim, Sherif M. M. H. Gomaa, Tarek Zayed, and Ghasan Alfalah

Abstract. Landslides rank among the most devastating hazards, leading to loss of life and destruction of infrastructure, with rainfall being a primary triggering factor. Global climate change has increased landslide occurrence; accordingly, accurate landslide prediction is crucial to reduce damage and losses. Since landslides account for 17 % of all natural hazard fatalities, several studies have been done across the globe to predict these events better. Despite the considerable number of review articles, a comprehensive comparison between empirically, physically, deterministically, and phenomenologically based prediction models is still missing. Moreover, they lack adopting mixed methodology. Accordingly, a mixed review that comprised scientometric, systematic, and bibliometric analysis was employed. This study (Part I of a two-part review) examines two approaches for analyzing local-scale landslides: empirical-statistical methods and physically based causative threshold models. Deterministic and phenomenologically based prediction models are discussed in part ii and have been published (Ebrahim et al., 2024a). This study explores the practicality and constraints associated with the aforementioned methodologies. As a result, critical insights into rainfall-induced landslides are examined. Macroscopically, antecedent rainfall surpasses the intensity-duration thresholds. Physically based causative thresholds can be utilized when geotechnical or hydrological data are limited. Microscopely, hybrid artificial intelligence models provide higher prediction accuracies. Finally, research suggestions are highlighted, as modeling artificial intelligence models with extensive datasets to achieve high prediction accuracy is still needed for further development.

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Kyrillos M. P. Ebrahim, Sherif M. M. H. Gomaa, Tarek Zayed, and Ghasan Alfalah

Status: open (until 07 Mar 2025)

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Kyrillos M. P. Ebrahim, Sherif M. M. H. Gomaa, Tarek Zayed, and Ghasan Alfalah
Kyrillos M. P. Ebrahim, Sherif M. M. H. Gomaa, Tarek Zayed, and Ghasan Alfalah

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Short summary
Rainfall induced landslides pose increasing risks to lives and infrastructure due to climate change. This study presents a framework for predicting landslides, combining statistical and physically based models. A flowchart demonstrates how large-scale models transition to localized. By analyzing thresholds and model performance, the study highlights the role of artificial intelligence and big data in improving predictions, helping to reduce global landslide risks.
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