Brief communication: Introducing rainfall thresholds for landslide triggering based on artificial neural networks
Pierpaolo Distefano, David J. Peres, Pietro Scandura, Antonino Cancelliere
Abstract
Abstract. In this communication we show how the use of artificial neural networks (ANNs) can improve the performance of the rainfall thresholds for landslide early warning. Results for Sicily (Italy) show how performance of a traditional rainfall event duration and depth power law threshold, yielding a true skill statistic (TSS) of 0.50, can be improved by ANNs (TSS = 0.59). Then we show how ANNs allow other variables to be easily added, like peak rainfall intensity, with a further performance improvement (TSS = 0.66). This may stimulate more research on the use of this powerful tool for deriving landslide early warning thresholds.
Topics & Concepts
LandslideStatisticArtificial neural networkWarning systemDuration (music)Computer scienceEnvironmental scienceStatisticsArtificial intelligenceSeismologyTelecommunicationsGeologyMathematicsAcousticsPhysicsLandslides and related hazardsFlood Risk Assessment and ManagementCryospheric studies and observations