Articles | Volume 22, issue 4
https://doi.org/10.5194/nhess-22-1151-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-22-1151-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Brief communication
| 
04 Apr 2022
Brief communication |
| 04 Apr 2022
| 04 Apr 2022
Brief communication: Introducing rainfall thresholds for landslide triggering based on artificial neural networks
Pierpaolo Distefano
Department of Civil Engineering and Architecture, University of Catania, Catania, 95123, Italy
Department of Civil Engineering and Architecture, University of Catania, Catania, 95123, Italy
Pietro Scandura
Department of Civil Engineering and Architecture, University of Catania, Catania, 95123, Italy
Antonino Cancelliere
Department of Civil Engineering and Architecture, University of Catania, Catania, 95123, Italy
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Cited
18 citations as recorded by crossref.
- Use of delayed ERA5-Land soil moisture products for improving landslide early warning N. Palazzolo et al. https://doi.org/10.5194/nhess-25-4907-2025
- Modelling the rainfall threshold for shallow landslides considering the landslide predisposing factors in Portugal C. Villaça et al. https://doi.org/10.1007/s10346-024-02284-y
- Construction and preliminary analysis of landslide database triggered by heavy storm in the parallel range-valley area of western Chongqing, China, on 8 June 2017 J. Liu & C. Xu https://doi.org/10.3389/feart.2024.1420425
- Exploring the Use of Pattern Classification Approaches for the Recognition of Landslide-Triggering Rainfalls A. Rosi https://doi.org/10.3390/su152015145
- Using Deep Learning to Formulate the Landslide Rainfall Threshold of the Potential Large-Scale Landslide J. Chiang et al. https://doi.org/10.3390/w14203320
- A Novel Safety Early Warning Methodology for Pipelines under Landslide Geological Hazard S. Liu et al. https://doi.org/10.1061/JPSEA2.PSENG-1529
- Evaluating the applicability of simulated soil moisture index in forecasting post-earthquake debris flows Z. Wei et al. https://doi.org/10.1016/j.enggeo.2026.108557
- DEWS: A QGIS tool pack for the automatic selection of reference rain gauges for landslide-triggering rainfall thresholds O. Al-Thuwaynee et al. https://doi.org/10.1016/j.envsoft.2023.105657
- An Update on Rainfall Thresholds for Rainfall-Induced Landslides in the Southern Apuan Alps (Tuscany, Italy) Using Different Statistical Methods R. Giannecchini et al. https://doi.org/10.3390/w16050624
- Spatio-temporal variability of landslides as indicator of climate change impact: Towards an Italian national scale integrated procedure for the rainfall role analysis in landslide dynamics M. Delchiaro et al. https://doi.org/10.1016/j.ijdrr.2026.106252
- Hydro-meteorological landslide triggering thresholds based on artificial neural networks using observed precipitation and ERA5-Land soil moisture P. Distefano et al. https://doi.org/10.1007/s10346-023-02132-5
- Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models S. Steger et al. https://doi.org/10.5194/nhess-23-1483-2023
- A novel prototype national-scale landslide nowcasting system for Italy combining rainfall thresholds and risk indicators S. Segoni et al. https://doi.org/10.1007/s10346-024-02452-0
- Towards landslide space-time forecasting through machine learning: the influence of rainfall parameters and model setting N. Nocentini et al. https://doi.org/10.3389/feart.2023.1152130
- Comparative evaluation of IMERG and MSWEP precipitation datasets for rainfall threshold development in Landslide-Prone Areas of Badung Regency P. Aryastana et al. https://doi.org/10.1088/1755-1315/1589/1/012008
- Regional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway) N. Nocentini et al. https://doi.org/10.1007/s10346-024-02287-9
- Towards Establishing Empirical Rainfall Thresholds for Shallow Landslides in Guangzhou, Guangdong Province, China R. Deng et al. https://doi.org/10.3390/w14233914
- 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 N. Palazzolo et al. https://doi.org/10.5194/nhess-23-279-2023
18 citations as recorded by crossref.
- Use of delayed ERA5-Land soil moisture products for improving landslide early warning N. Palazzolo et al. https://doi.org/10.5194/nhess-25-4907-2025
- Modelling the rainfall threshold for shallow landslides considering the landslide predisposing factors in Portugal C. Villaça et al. https://doi.org/10.1007/s10346-024-02284-y
- Construction and preliminary analysis of landslide database triggered by heavy storm in the parallel range-valley area of western Chongqing, China, on 8 June 2017 J. Liu & C. Xu https://doi.org/10.3389/feart.2024.1420425
- Exploring the Use of Pattern Classification Approaches for the Recognition of Landslide-Triggering Rainfalls A. Rosi https://doi.org/10.3390/su152015145
- Using Deep Learning to Formulate the Landslide Rainfall Threshold of the Potential Large-Scale Landslide J. Chiang et al. https://doi.org/10.3390/w14203320
- A Novel Safety Early Warning Methodology for Pipelines under Landslide Geological Hazard S. Liu et al. https://doi.org/10.1061/JPSEA2.PSENG-1529
- Evaluating the applicability of simulated soil moisture index in forecasting post-earthquake debris flows Z. Wei et al. https://doi.org/10.1016/j.enggeo.2026.108557
- DEWS: A QGIS tool pack for the automatic selection of reference rain gauges for landslide-triggering rainfall thresholds O. Al-Thuwaynee et al. https://doi.org/10.1016/j.envsoft.2023.105657
- An Update on Rainfall Thresholds for Rainfall-Induced Landslides in the Southern Apuan Alps (Tuscany, Italy) Using Different Statistical Methods R. Giannecchini et al. https://doi.org/10.3390/w16050624
- Spatio-temporal variability of landslides as indicator of climate change impact: Towards an Italian national scale integrated procedure for the rainfall role analysis in landslide dynamics M. Delchiaro et al. https://doi.org/10.1016/j.ijdrr.2026.106252
- Hydro-meteorological landslide triggering thresholds based on artificial neural networks using observed precipitation and ERA5-Land soil moisture P. Distefano et al. https://doi.org/10.1007/s10346-023-02132-5
- Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models S. Steger et al. https://doi.org/10.5194/nhess-23-1483-2023
- A novel prototype national-scale landslide nowcasting system for Italy combining rainfall thresholds and risk indicators S. Segoni et al. https://doi.org/10.1007/s10346-024-02452-0
- Towards landslide space-time forecasting through machine learning: the influence of rainfall parameters and model setting N. Nocentini et al. https://doi.org/10.3389/feart.2023.1152130
- Comparative evaluation of IMERG and MSWEP precipitation datasets for rainfall threshold development in Landslide-Prone Areas of Badung Regency P. Aryastana et al. https://doi.org/10.1088/1755-1315/1589/1/012008
- Regional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway) N. Nocentini et al. https://doi.org/10.1007/s10346-024-02287-9
- Towards Establishing Empirical Rainfall Thresholds for Shallow Landslides in Guangzhou, Guangdong Province, China R. Deng et al. https://doi.org/10.3390/w14233914
- 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 N. Palazzolo et al. https://doi.org/10.5194/nhess-23-279-2023
Saved (final revised paper)
Latest update: 09 Jun 2026
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.
In the communication, we introduce the use of artificial neural networks (ANNs) for improving...
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