Chrono-environnement - UMR 6249 CNRS/UFC
Campus de la Bouloie
16 rue de Gray
25030 Besançon cedex
France
Olivier Béjean-Maillard
PhD Student
GEODE
olivier.maillard@-Code to remove to avoid SPAM-univ-fcomte.fr
+33 (0)3 81 66 64 31, room -227L (La Bouloie)
Chrono Supervisor Chrono :
0009-0007-9264-5488 
obejeanmaillard
Geohazards – Landslides – Time forecast models – Meteorological and hydrogeological features – Machine learning
Presentation
PhD thesis: Detection of precursors to the triggering of gravity instabilities: multi-parameter chronicles, artificial intelligence and modelling.
Landslides are gravity movements that can be triggered by factors such as seismic activity and climatic events (precipitation, storms, etc.). With the aim of improving landslide prevention systems over time, my thesis work aims to identify the hydrogeological and meteorological factors that are precursors to instability phases. To do this, I am building a model for predicting displacement speeds over time using machine learning models (Random Forest, XGBoost) to rank the factors most used in the prediction, initially using data from the Séchilienne landslide.
▶ See more on the HAL CV
Publications
Journal articles
2025
- ref_biblio
- Olivier Béjean-Maillard, Catherine Bertrand, Jean-Philippe Malet, Guillaume Cinkus, Pierre Nevers, et al.. Hydrogeological forecasting of deep-seated landslides dynamics: structure and sensitivity of tank models. Landslides, 2025, ⟨10.1007/s10346-025-02482-2⟩. ⟨hal-04976057⟩
- Accès au bibtex
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Conference papers
2024
- ref_biblio
- Joshua Ducasse, Catherine Bertrand, Olivier Maillard, Delphine Charpentier, Jean-Pierre Sizun, et al.. HYDROGEOLOGICAL SIMULATIONS OF A DEEP-SEATED LANDSLIDE: IMPLICATIONS FOR HAZARD MITIGATION. XIVth International Symposium on Landslides, Jul 2024, Chambéry (France), France. ⟨hal-04938130⟩
- Accès au texte intégral et bibtex
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- ref_biblio
- Olivier Maillard, Catherine Bertrand, Jean-Philippe Malet. Forecasting landslide motion with machine learning models: the use case of Séchilienne landslide (French Alps) to identify the relevant predicting variables. XIVth International Symposium on Landslides, Jul 2024, Chambéry (France), France. ⟨hal-04938136⟩
- Accès au texte intégral et bibtex
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- ref_biblio
- Olivier Maillard, Catherine Bertrand, Jean-Philippe Malet. Forecasting landslide motion with EXplainable Machine Learning models: the use case of Séchilienne landslide (French Alps) to identify the relevant predicting variables. European Geolosciences Union, Apr 2024, Viennes, Austria. ⟨10.5194/egusphere-egu24-16825⟩. ⟨hal-04976803⟩
- Accès au bibtex
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