Articles | Volume 22, issue 4
https://doi.org/10.5194/nhess-22-1151-2022
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, David J. Peres, Pietro Scandura, and Antonino Cancelliere

Related authors

Using principal component analysis to incorporate multi-layer soil moisture information in hydrometeorological thresholds for landslide prediction: an investigation based on ERA5-Land reanalysis data
Nunziarita Palazzolo, David J. Peres, Enrico Creaco, and Antonino Cancelliere
Nat. Hazards Earth Syst. Sci., 23, 279–291, https://doi.org/10.5194/nhess-23-279-2023,https://doi.org/10.5194/nhess-23-279-2023, 2023
Short summary
Brief communication: Key papers of 20 years in Natural Hazards and Earth System Sciences
Animesh K. Gain, Yves Bühler, Pascal Haegeli, Daniela Molinari, Mario Parise, David J. Peres, Joaquim G. Pinto, Kai Schröter, Ricardo M. Trigo, María Carmen Llasat, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 22, 985–993, https://doi.org/10.5194/nhess-22-985-2022,https://doi.org/10.5194/nhess-22-985-2022, 2022
Short summary
Evaluation of EURO-CORDEX (Coordinated Regional Climate Downscaling Experiment for the Euro-Mediterranean area) historical simulations by high-quality observational datasets in southern Italy: insights on drought assessment
David J. Peres, Alfonso Senatore, Paola Nanni, Antonino Cancelliere, Giuseppe Mendicino, and Brunella Bonaccorso
Nat. Hazards Earth Syst. Sci., 20, 3057–3082, https://doi.org/10.5194/nhess-20-3057-2020,https://doi.org/10.5194/nhess-20-3057-2020, 2020
Short summary
Influence of uncertain identification of triggering rainfall on the assessment of landslide early warning thresholds
David J. Peres, Antonino Cancelliere, Roberto Greco, and Thom A. Bogaard
Nat. Hazards Earth Syst. Sci., 18, 633–646, https://doi.org/10.5194/nhess-18-633-2018,https://doi.org/10.5194/nhess-18-633-2018, 2018
Short summary
Derivation and evaluation of landslide-triggering thresholds by a Monte Carlo approach
D. J. Peres and A. Cancelliere
Hydrol. Earth Syst. Sci., 18, 4913–4931, https://doi.org/10.5194/hess-18-4913-2014,https://doi.org/10.5194/hess-18-4913-2014, 2014
Short summary

Related subject area

Landslides and Debris Flows Hazards
Lessons learnt from a rockfall time series analysis: data collection, statistical analysis, and applications
Sandra Melzner, Marco Conedera, Johannes Hübl, and Mauro Rossi
Nat. Hazards Earth Syst. Sci., 23, 3079–3093, https://doi.org/10.5194/nhess-23-3079-2023,https://doi.org/10.5194/nhess-23-3079-2023, 2023
Short summary
The concept of event-size-dependent exhaustion and its application to paraglacial rockslides
Stefan Hergarten
Nat. Hazards Earth Syst. Sci., 23, 3051–3063, https://doi.org/10.5194/nhess-23-3051-2023,https://doi.org/10.5194/nhess-23-3051-2023, 2023
Short summary
Coastal earthquake-induced landslide susceptibility during the 2016 Mw 7.8 Kaikōura earthquake, New Zealand
Colin K. Bloom, Corinne Singeisen, Timothy Stahl, Andrew Howell, Chris Massey, and Dougal Mason
Nat. Hazards Earth Syst. Sci., 23, 2987–3013, https://doi.org/10.5194/nhess-23-2987-2023,https://doi.org/10.5194/nhess-23-2987-2023, 2023
Short summary
Characteristics of debris flows recorded in the Shenmu area of central Taiwan between 2004 and 2021
Yi-Min Huang
Nat. Hazards Earth Syst. Sci., 23, 2649–2662, https://doi.org/10.5194/nhess-23-2649-2023,https://doi.org/10.5194/nhess-23-2649-2023, 2023
Short summary
Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine
Davide Notti, Martina Cignetti, Danilo Godone, and Daniele Giordan
Nat. Hazards Earth Syst. Sci., 23, 2625–2648, https://doi.org/10.5194/nhess-23-2625-2023,https://doi.org/10.5194/nhess-23-2625-2023, 2023
Short summary

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. 
Download
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.
Altmetrics
Final-revised paper
Preprint