Forecasting landslide motion with EXplainable Machine Learning models: the use case of Séchilienne landslide (French Alps) to identify the relevant predicting variables

Recent works on landslide displacement forecasting using machine learning or deep learning models show relevant performance. However, they are mostly based on the use of historical displacement information and do not provide information on the most predictive features in terms of meteorological and hydrogeological variables for the forecast, and thus the identification of possible precursory factors. In this context, providing approaches based on EXplainable Machine Learning (XML) is essential for landslide forecasting as it concerns making decisions about risk mitigation actions, it supports the identification of possible precursory factors and it increases confidence in the predictions.
The proposed XML-based landslide forecasting approach is developed and tested using ensemble learning methods such as Random Forest and XGBoost. It relies on the use of multi-year and multi-parameter data chronicles to analyse the relationships between surface displacements (target data) and hydro-meteorological conditions (predictor data). Displacement and meteorological data are acquired through the landslide monitoring network. Hydrological data, when not available, are simulated discharge calculated with reservoir based-model; the simulations allow to construct water level time series for each water reservoirs identified in the unstable slope.
The predictive time series are decomposed into a set of 340 descriptive features (mean, variance, difference, number of rainy days, number of consecutive rainy periods of X days, …). The displacement time series are detrended using the multiplicative decomposition method.
This method has been applied to several use cases, such as the Séchilienne landslide located southwest of the Belledonne massif (French Alps). The Random forest and XGBoost models are trained and tested over periods of 12 and 5 years respectively, and applied to three automatic extensometers located in the most active part of the landslide. The results indicate that the main features used include variations in water levels over past 10 to 30 days, as well as the number of consecutive rainy period during the month. These results are associated with accurate predictions for the three extensometers, with coefficients of determination ranging between 0.37 and 0.46.
We show that these models have high predictive power while informing about the most important hydro-meteorological features. The application of the models to trendless displacement time series significantly improves prediction accuracy.