Speech-recognition in landslide predictive modelling: A case for a next generation early warning system
Zhice Fang, Hakan Tanyaş, Tolga Görüm, Ashok Dahal, Yi Wang, Luigi Lombardo
Abstract
Traditional landslide early warnings are based on the notion that intensity-duration relations can be approximated to single precipitation values cumulated over fixed time windows. Here, we take on a similar task being inspired by modeling architectures typical of speech-recognition tasks. We aim at classifying the Turkish landscape into 5 km grids assigned with a landslide susceptibility estimate. We collected all available national information on precipitation-induced landslide occurrences. This information is passed to a Long Short-Term Memory equipped with the whole rainfall time series, obtained from daily CHIRPS data. We test this model randomizing the presence/absence data to represent the slope instability over Turkey and over 13 years under consideration (2008–2020) and assessing different time windows. Results show that the inclusion of the full precipitation signal rather than its scalar approximation leads to a substantial increase in prediction power (approximately 20%). This may potentially pave the road for a new generation of speech-recognition-based landslide early warning systems.