Journal
ENVIRONMENTAL MODELLING & SOFTWARE
Volume 170, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.envsoft.2023.105833
Keywords
Landslide prediction; Precipitation; Speech recognition; Time series; Early warning system
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Traditional landslide early warning systems approximate precipitation-induced landslides based on single precipitation values, while this study uses a modeling architecture inspired by speech-recognition tasks to improve prediction power by considering the full rainfall signal, potentially paving the way for a new generation of speech-recognition-based landslide early warning systems.
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 dynamic landslide susceptibility estimates. We collected all available national information on precipitation-induced landslide occurrences. This information is passed to a Long ShortTerm Memory equipped with the whole rainfall time series, obtained from daily CHIRPS data. We test this model: 1) by randomizing the presence/absence data to represent the slope instability over Turkey and over 13 years under consideration (2008-2020) and 2) by assessing the effect of different time windows used to pass the rainfall signal to the neural network. 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.
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