1,- 1Faculty of Sciences and Technologies, Cadi Ayyad University, Marrakech, Morocco
- 2Institute of Geography, University of Hamburg, Hamburg, Germany
- 3National school of applied sciences, Mohamed Premier University, Oujda, Morocco
- 4Mohammed VI Polytechnic University, Benguerir, Morocco
Landslides are a global geological phenomenon that constitute serious threats for human lives and engineering infrastructure, making the susceptibility assessment of these landslides a critical step for risk mitigation. The Al Haouz province, which was heavily struck by the Mw 6.8 earthquake of 2023, recorded several slope instabilities caused by seismic motion. In this context, the present study aims to evaluate co-seismic landslides susceptibility using machine learning models to support effective risk mitigations.
Logistic Regression LR and Random Forest RF models were employed to generate the susceptibility maps. The landslide inventory map with 302 landslide points and 600 non-landslide points was utilized with a 70:30 split for training/testing purposes. Sixteen conditioning factors were considered in the modelling process.
The results indicate RF performed better than the LR method, with an accuracy of 97.34% compared 92.92% for LR. The area under the curve (AUC) values ranged between 0.98 for LR and 0.99 for RF. reflecting the high predictive capability of both models. Elevation, Slope, PGA and rainfall had the highest contribution scores amongst the factors identified by both models.
The outcomes indicate the effectiveness of machine learning algorithms, specifically the RF model, for susceptibility mapping related to landslides in a seismic area. Elevation and slope are the most important factors influencing landslides from a geomorphological perspective in Al Haouz province. PGA is the most significant parameter among all factors as landslides are primarily triggered by seismic acceleration associated with earthquake events. Rainfall is a significant parameter that triggers landslides as a result of steep slopes associated with heavy rainfall either continuously or with high intensity.
The co-seismic landslide susceptibility maps produced in this study provide valuable information for identifying vulnerable zones and constitute an effective tool for land-use planning and disaster risk reduction aimed at protecting human lives, infrastructure, and the environment.
Keywors: Landslide susceptibility; Al Haouz earthquake; Machine learning; Morocco
How to cite: Edoudi, A., Cherif, S., Ibouh, H., Ahmadian, N., El Wahidi, F., Chourak, M., Kurtz, R., and Dubovyk, O.: Co-seismic Landslide Susceptibility Mapping after the 2023 Al Haouz Earthquake (Morocco) Using Machine Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13589, https://doi.org/10.5194/egusphere-egu26-13589, 2026.
