Rock slope dynamics in flysch formation under cold climate (part 3) : rockfall forecasting

Rockfalls are major natural hazards for road users and infrastructures in northern Gaspésie (Eastern Canada) where nearly 15 kilometers of road runs along 10 to 100 m high flysch rockwall. The Ministère des Transports du Québec (MTQ) has recorded more than 17 500 rockfalls that have reached the roadway since 1987, which represents a nearly permanent danger for users. In the late 90s, protective berms were erected to reduce the number of rocks reaching the roadway. Despite the efficiency of these infrastructures, more than a hundred events are still recorded each year. Based on previous studies showing that rock instabilities in this type of geology is strongly correlated with meteorological events, we used different machine learning methods (logistic regression, classification tree, random forest, neural network) to design the best operational rockfall prediction model. Three event variables based on different rock fall frequency and magnitude thresholds were created. Nearly one hundred weather variables were used to explain and predict events. Preliminary results show that thawing degree-days is one of the most effective variables explaining the occurrence of winter and spring rockfall events. In summer, rainfall intensity is the most potential explanatory variable. Finally, fall events appear to be more responsive to rain events and freeze-thaw cycles. In order to optimize the percentage of predicted events and reduce the false alarm ratio, it remains important to evaluate the impact of each parameter on the performance of the models. These models can be used operationally as decision support tools to predict days with high event probability.