Brief communication: Introducing rainfall thresholds for landslide triggering based on artificial neural networks

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 s...

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Bibliographic Details
Published inNatural hazards and earth system sciences Vol. 22; no. 4; pp. 1151 - 1157
Main Authors Distefano, Pierpaolo, Peres, David J, Scandura, Pietro, Cancelliere, Antonino
Format Journal Article
LanguageEnglish
Published Katlenburg-Lindau Copernicus GmbH 04.04.2022
Copernicus Publications
Subjects
Artificial intelligence
Artificial neural networks
Communication
Emergency communications systems
Gauges
Landslide warnings
Landslides
Landslides & mudslides
Machine learning
Mathematical functions
Neural networks
Performance enhancement
Precipitation
Rain
Rain and rainfall
Rainfall
Rainfall intensity
Software
Thresholds
Italy
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Summary: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.
ISSN:1684-9981
1561-8633
1684-9981
DOI:10.5194/nhess-22-1151-2022