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

Viewed

Total article views: 2,082 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,309 715 58 2,082 59 50
  • HTML: 1,309
  • PDF: 715
  • XML: 58
  • Total: 2,082
  • BibTeX: 59
  • EndNote: 50
Views and downloads (calculated since 12 Jul 2021)
Cumulative views and downloads (calculated since 12 Jul 2021)

Viewed (geographical distribution)

Total article views: 2,082 (including HTML, PDF, and XML) Thereof 1,968 with geography defined and 114 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 May 2024
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