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                <term xml:lang="en">Séchilienne landslide</term>
                <term xml:lang="en">Machine Learning</term>
                <term xml:lang="en">Forecast</term>
                <term xml:lang="en">Landslide motion</term>
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              <p>&lt;div&gt;&lt;p&gt;The Séchilienne landslide is located in the Romanche valley, south-west of Belledonne massif and 20km south-east of Grenoble. The highly fractured nature of the landslide and the difference in hydraulic conductivity between fractured and micro-fissured rocks lead to the development of a perched aquifer located in the unstable zone. Pore water pressure variations of the perched aquifer led to a decrease in rock properties and are a key factor in landslide initiation. Thirty years of multi-parametric observations are available allowing to analyse the variation of terrain motion in relation to daily meteorological and hydrogeological conditions. These datasets are used to forecast landslide motion with supervised machine learning methods. The use of ensemble learning methods such as Random Forest and XGBoost allow to propose forecasting models while providing information about the most important predicting features. The features introduced in the model are described and discussed as well as the model calibration/validation strategy. We show that models combining meteorological and hydrogeological features obtained with a reservoir hydrological model have high predictive power.&lt;/p&gt;&lt;/div&gt;</p>
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              <p>Le glissement de Séchilienne est situé dans la vallée de la Romanche, au sud-ouest du massif de Belledonne et à 20 km au sud-est de Grenoble. La nature hautement fracturée du glissement de terrain et la différence de conductivité hydraulique entre les roches fracturées et micro-fissurées ont conduit au développement d'un aquifère perché situé dans la zone instable. Les variations de la pression de l'eau interstitielle de l'aquifère perché ont entraîné une diminution des propriétés de la roche et constituent un facteur clé dans le déclenchement des glissements de terrain. Trente années d'observations multiparamétriques sont disponibles et permettent d'analyser la variation des mouvements de terrain en fonction des conditions météorologiques et hydrogéologiques quotidiennes. Ces ensembles de données sont utilisés pour prévoir le mouvement des glissements de terrain à l'aide de méthodes d'apprentissage automatique supervisé. L'utilisation de méthodes d'apprentissage d'ensemble telles que Random Forest et XGBoost permet de proposer des modèles de prévision tout en fournissant des informations sur les caractéristiques prédictives les plus importantes. Les caractéristiques introduites dans le modèle sont décrites et discutées ainsi que la stratégie de calibration/validation du modèle. Nous montrons que les modèles combinant les caractéristiques météorologiques et hydrogéologiques obtenues à l'aide d'un modèle hydrologique de réservoir ont un pouvoir prédictif élevé.</p>
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