Machine Learning Analysis of Landslide Susceptibility in the Western Québec Seismic Zone of Canada

Landslide hazard potential is high across the St. Lawrence lowlands of Québec, Canada, due to sensitive glaciomarine clay deposits and the presence of moderate seismic activity, causing slope failures in the region. The main objectives of the study are to develop a working database for landslides in the region and use that database to improve regional landslide susceptibility analysis. Using high-resolution (1 m by 1 m cells) digital terrain models dated from 2009 and validated with satellite photogrammetry from 2012, a landslide inventory of 263 cases related to the 2010 Val-des-Bois earthquake (moment magnitude 5.0) is created. Relationships between landslide susceptibility factors, such as slope angle, and seismic conditioning factors, such as peak ground acceleration, are examined through machine learning methods. For landslide detection, an overall accuracy of approximately 85% (AUC 0.914) is achieved using random forest and logistic regression models cross-validated through 5-fold analysis, showing improvement over the currently employed Hazus method, which achieves an accuracy of approximately 67%. From a regional perspective, the developed inventory and resultant susceptibility models are unique and form the foundation for future studies to improve the understanding of earthquake-induced landslides in the Western Québec Seismic Zone, which historically lacks detailed landslide inventories.