Can machine learning improve debris flow warning?

Machine learning could provide up an extra hour of warning time for debris flows along the Illgraben torrent in Switzerland, researchers report at the Seismological Society of America (SSA)'s 2021 Annual Meeting. Debris flows are mixtures of water, sediment and rock that move rapidly down steep hills, triggered by heavy precipitation and often containing tens of thousands of cubic meters of material. Their destructive potential makes it important to have monitoring and warning systems in place to protect nearby people and infrastructure. In her presentation at SSA, Ma?gorzata Chmiel of ETH Zürich described a machine learning approach to detecting and alerting against debris flows for the Illgraben torrent, a site in the European Alps that experiences significant debris flows and torrential events each year. Seismic records from stations located in the Illgraben catchment, from 20 previous debris flow events, were used to train an algorithm to recognize the seismic signals of debris flow formation, accurately detecting early flows 90% of the time. The machine learning system was able to detect all 13 debris flows and torrential events that occurred during a three-month period in 2020.