Articles | Volume 22, issue 4
Nat. Hazards Earth Syst. Sci., 22, 1151–1157, 2022
https://doi.org/10.5194/nhess-22-1151-2022
Nat. Hazards Earth Syst. Sci., 22, 1151–1157, 2022
https://doi.org/10.5194/nhess-22-1151-2022
Brief communication
04 Apr 2022
Brief communication | 04 Apr 2022

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

Pierpaolo Distefano et al.

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Cited articles

Bogaard, T. and Greco, R.: Invited perspectives: Hydrological perspectives on precipitation intensity-duration thresholds for landslide initiation: proposing hydro-meteorological thresholds, Nat. Hazards Earth Syst. Sci., 18, 31–39, https://doi.org/10.5194/nhess-18-31-2018, 2018. 
Caine, N.: The Rainfall Intensity-Duration Control of Shallow Landslides and Debris Flows, Soc. Swedish Ann. Geogr. Geogr. Phys., 62, 23–27, 1980. 
Calvello, M. and Pecoraro, G.: FraneItalia: a catalog of recent Italian landslides, Geoenviron. Disast., 5, 13, https://doi.org/10.1186/s40677-018-0105-5, 2018. 
Calvello, M. and Pecoraro, G.: The FraneItalia database, FraneItalia [data set], https://franeitalia.wordpress.com/database/, last access: 17 November 2021. 
Conrad, J. L., Morphew, M. D., Baum, R. L., and Mirus, B. B.: HydroMet: A New Code for Automated Objective Optimization of Hydrometeorological Thresholds for Landslide Initiation, Water, 13, 1752, https://doi.org/10.3390/W13131752, 2021. 
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Short summary
In the communication, we introduce the use of artificial neural networks (ANNs) for improving the performance of rainfall thresholds for landslide early warning. Results show how ANNs using rainfall event duration and mean intensity perform significantly better than a classical power law based on the same variables. Adding peak rainfall intensity as input to the ANN improves performance even more. This further demonstrates the potentialities of the proposed machine learning approach.
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