Abstract
Landslide prediction is key for the development of early warning systems. In this work, we develop artificial neural networks (ANNs) that can identify landslide triggering conditions using soil moisture data in addition to precipitation. In particular, we use observed precipitation and ERA5-Land reanalysis soil moisture data at four different depth layers at the beginning and end of the precipitation events. Two different case studies, Sicily region (Italy), and a group of catchments in the Bergen area (Norway), are used to test the proposed approach against different climatic and geomorphological conditions. As a first step, traditional power law thresholds based on cumulative precipitation and duration (E-D) are derived by maximizing the true skill statistic (TSS) as a benchmark. For both study areas, ANNs using 87 different input combinations of precipitation characteristics and soil moisture data at multiple depth layers are analyzed. The developed ANN classifiers using soil moisture information in addition to precipitation outperform those using precipitation data only. Specifically, while power law E-D thresholds lead to a TSS maximum of 0.50 for both areas, the use of single-layer soil moisture yields a maximum TSS of 0.76 (0.78) for Sicily (Bergen area), while the use of multilayer soil moisture taken at both the start and the end of precipitation events yields a TSS = 0.79 (0.89). These results demonstrate that the proposed methodology is particularly promising for improving landslide prediction.
This is a preview of subscription content, log in via an institution to check access.
Data availability
Landslide data are available at https://franeitalia.wordpress.com/ (Italy) and https://nedlasting.nve.no/gis/ (Norway). Precipitation data for Norway are available at https://thredds.met.no/thredds/catalog/senorge/seNorge_2018/catalog.html, while for Sicily are available from several sources (http://www.sias.regione.sicilia.it/, https://www.protezionecivilesicilia.it:8443/aegis/map/map2d). Codes and datasets can be made available upon request to the authors.
References
Aleotti P (2004) A warning system for rainfall-induced shallow failures. Eng Geol 73:247–265. https://doi.org/10.1016/J.ENGGEO.2004.01.007
Beck HE, Pan M, Miralles DG, Reichle RH, Dorigo WA, Hahn S, Sheffield J, Karthikeyan L, Balsamo G, Parinussa RM, van Dijk AIJM, Du J, Kimball JS, Vergopolan N, Wood EF (2021) Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors. Hydrol Earth Syst Sci 25:17–40. https://doi.org/10.5194/hess-25-17-2021
Bogaard T, Greco R (2018) 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
Brocca L, Ciabatta L, Moramarco T, Ponziani F, Berni N, Wagner W (2016) Use of satellite soil moisture products for the operational mitigation of landslides risk in Central Italy, satellite soil moisture retrieval: techniques and applications. Elsevier Inc. https://doi.org/10.1016/B978-0-12-803388-3.00012-7
Brunetti MT, Peruccacci S, Rossi M, Luciani S, Valigi D, F.G., (2010) Rainfall thresholds for the possible occurrence of landslides in Italy. Nat Hazards Earth Syst Sci 47:633–635. https://doi.org/10.1016/S1387-6473(03)00110-6
Caine N (1980) The rainfall intensity-duration control of shallow landslides and debris flows. Geogr Ann Ser A Phys Geogr 62:23–27
Calvello M, Pecoraro G (2018) FraneItalia: a catalog of recent Italian landslides. Geoenvironmen Disasters 5. https://doi.org/10.1186/s40677-018-0105-5
Campbell RH (1975) Debris flows originating from soil slips during rainstorms in Southern California. Q J Eng Geol 7:339–349. https://doi.org/10.1144/GSL.QJEG.1974.007.04.04
Collini E, Palesi LAI, Nesi P, Pantaleo G, Nocentini N, Rosi A (2022) Predicting and understanding landslide events with explainable AI. IEEE Access 10:31175–31189. https://doi.org/10.1109/ACCESS.2022.3158328
Conrad JL, Morphew MD, Baum RL, Mirus BB (2021) Hydromet: a new code for automated objective optimization of hydro-meteorological thresholds for landslide initiation. Water (Switzerland) 13. https://doi.org/10.3390/w13131752
Crow WT, Berg AA, Cosh MH, Loew A, Mohanty BP, Panciera R, De Rosnay P, Ryu D, Walker JP (2012) Upscaling sparse ground-based soil moisture observations for the validation of coarse-resolution satellite soil moisture products. Rev Geophys 50:1–20. https://doi.org/10.1029/2011RG000372
Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Masuda T, Nishino K (2009) Failure characteristics of rainfall-induced shallow landslides in granitic terrains of Shikoku island of Japan. Environ Geol 56:1295–1310. https://doi.org/10.1007/s00254-008-1228-x
Dai FC, Lee CF, Wang SJ (2003) Characterization of rainfall-induced landslides. Int J Remote Sens 24:4817–4834. https://doi.org/10.1080/014311601131000082424
Distefano P, Peres DJ, Scandura P, Cancelliere A (2022) Brief communication: introducing rainfall thresholds for landslide triggering based on artificial neural networks. Nat Hazards Earth Syst Sci 1151–1157
Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861–874. https://doi.org/10.1016/j.patrec.2005.10.010
Froude MJ, Petley DN (2018) Global fatal landslide occurrence from 2004 to 2016. Nat Hazards Earth Syst Sci 18:2161–2181. https://doi.org/10.5194/nhess-18-2161-2018
Gariano SL, Brunetti MT, Iovine G, Melillo M, Peruccacci S, Terranova O, Vennari C, Guzzetti F (2015) Calibration and validation of rainfall thresholds for shallow landslide forecasting in Sicily, southern Italy. Geomorphology 228:653–665. https://doi.org/10.1016/J.GEOMORPH.2014.10.019
Glade T, Crozier M, Smith P (2000) Applying probability determination to refine landslide-triggering rainfall thresholds using an empirical “antecedent daily rainfall model.” Pure Appl Geophys 157:1059–1079. https://doi.org/10.1007/s000240050017
Gomis-Cebolla J, Rattayova V, Salazar-Galán S, Francés F (2023) Evaluation of ERA5 and ERA5-land reanalysis precipitation datasets over Spain (1951–2020). Atmos Res 284. https://doi.org/10.5194/hess-23-207-2019
Guzzetti F, Peruccacci S, Rossi M, Stark CP (2007) Rainfall thresholds for the initiation of landslides in central and southern Europe. Meteorol Atmos Phys 98:239–267. https://doi.org/10.1007/s00703-007-0262-7
Haque U, da Silva PF, Devoli G, Pilz J, Zhao B, Khaloua A, Wilopo W, Andersen P, Lu P, Lee J, Yamamoto T, Keellings D, Jian-Hong W, Glass GE (2019) The human cost of global warming: deadly landslides and their triggers (1995–2014). Sci Total Environ 682:673–684. https://doi.org/10.1016/j.scitotenv.2019.03.415
Haykin S (1999) Neural networks- a comprehensive foundation 2nd rd. Prentice Hall
Huang W, Loveridge F, Satyanaga A (2022) Translational upper bound limit analysis of shallow landslides accounting for pore pressure effects. Comput Geotech 148:104841. https://doi.org/10.1016/j.compgeo.2022.104841
Jung M, Reichstein M, Ciais P, Seneviratne SI, Sheffield J, Goulden ML, Bonan G, Cescatti A, Chen J, De Jeu R, Dolman AJ, Eugster W, Gerten D, Gianelle D, Gobron N, Heinke J, Kimball J, Law BE, Montagnani L, Mu Q, Mueller B, Oleson K, Papale D, Richardson AD, Roupsard O, Running S, Tomelleri E, Viovy N, Weber U, Williams C, Wood E, Zaehle S, Zhang K (2010) Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature 467:951–954. https://doi.org/10.1038/nature09396
Kayyal MK (1991) Investigation of long-term strength properties of Paris and Beaumont clays in earth embankments. Research Report 1195–2F, Center for Transportation Research, University of Texas at Austin, Austin, Texas
Kline DM, Berardi VL (2005) Revisiting squared-error and cross-entropy functions for training neural network classifiers. Neural Comput Appl 14:310–318. https://doi.org/10.1007/s00521-005-0467-y
Longo-Minnolo G, Vanella D, Consoli S, Pappalardo S, Ramírez-Cuesta JM (2022) Assessing the use of ERA5-land reanalysis and spatial interpolation methods for retrieving precipitation estimates at basin scale. Atmos Res 271:106131. https://doi.org/10.1016/j.atmosres.2022.106131
Lussana C, Einar Tveito O, Dobler A, Tunheim K (2019) SeNorge_2018, daily precipitation, and temperature datasets over Norway. Earth Syst Sci Data 11:1531–1551. https://doi.org/10.5194/essd-11-1531-2019
Marino P, Peres DJ, Cancelliere A, Greco R, Bogaard TA (2020). Soil moisture information can improve shallow landslide forecasting using the hydro-meteorological threshold approach. https://doi.org/10.1007/s10346-020-01420-8
Melillo M, Brunetti MT, Peruccacci S, Gariano SL, Guzzetti F (2016) Rainfall thresholds for the possible landslide occurrence in Sicily (Southern Italy) based on the automatic reconstruction of rainfall events. Landslides 13:165–172. https://doi.org/10.1007/s10346-015-0630-1
Melillo M, Brunetti MT, Peruccacci S, Gariano SL, Guzzetti F (2015) An algorithm for the objective reconstruction of rainfall events responsible for landslides. Landslides 12:311–320. https://doi.org/10.1007/s10346-014-0471-3
Melillo M, Brunetti MT, Peruccacci S, Gariano SL, Roccati A, Guzzetti F (2018) A tool for the automatic calculation of rainfall thresholds for landslide occurrence. Environ Model Softw 105:230–243. https://doi.org/10.1016/J.ENVSOFT.2018.03.024
Meyer NK, Dyrrdal AV, Frauenfelder R, EtzelmÃller B, Nadim F (2012) Hydro-meteorological threshold conditions for debris flow initiation in Norway. Nat Hazards Earth Syst Sci 12:3059–3073. https://doi.org/10.5194/nhess-12-3059-2012
Mirus BB, Becker RE, Baum RL, Smith JB (2018a) Integrating real-time subsurface hydrologic monitoring with empirical rainfall thresholds to improve landslide early warning. Landslides 15:1909–1919. https://doi.org/10.1007/s10346-018-0995-z
Mirus BB, Morphew MD, Smith JB (2018b) Developing hydro-meteorological thresholds for shallow landslide initiation and early warning. Water (switzerland) 10:1–19. https://doi.org/10.3390/W10091274
Møller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning, neural networks. https://doi.org/10.1016/S0893-6080(05)80056-5
Muñoz-Sabater J, Dutra E, Agustí-Panareda A, Albergel C, Arduini G, Balsamo G, Boussetta S, Choulga M, Harrigan S, Hersbach H, Martens B, Miralles DG, Piles M, Rodríguez-Fernández NJ, Zsoter E, Buontempo C, Thépaut JN (2021) ERA5-land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst Sci Data 13:4349–4383. https://doi.org/10.5194/essd-13-4349-2021
Palazzolo N, Peres DJ, Creaco E, Cancelliere A (2023) Using principal component analysis to incorporate multi-layer soil moisture information in hydro-meteorological thresholds for landslide prediction: an investigation based on ERA5-land reanalysis data. Nat Hazards Earth Syst Sci 1–22
Pelosi A, Terribile F, D’Urso G, Chirico GB (2020) Comparison of ERA5-land and UERRA MESCAN-SURFEX reanalysis data with spatially interpolated weather observations for the regional assessment of reference evapotranspiration. Water (Switzerland) 12. https://doi.org/10.3390/W12061669
Peres DJ, Cancelliere A (2021) Comparing methods for determining landslide early warning thresholds: potential use of non-triggering rainfall for locations with scarce landslide data availability. Landslides. https://doi.org/10.1007/s10346-021-01704-7
Peres DJ, Cancelliere A, Greco R, Bogaard TA (2018) Influence of uncertain identification of triggering rainfall on the assessment of landslide early warning thresholds. Nat Hazards Earth Syst Sci 18:633–646. https://doi.org/10.5194/nhess-18-633-2018
Perry J (1989) A survey of slope condition on motorway earthworks in England and Wales. Research Report 199. Transport & Road Research Laboratory, Crowthorne
Peruccacci S, Brunetti MT, Gariano SL, Melillo M, Rossi M, Guzzetti F (2017) Rainfall thresholds for possible landslide occurrence in Italy. Geomorphology 290:39–57. https://doi.org/10.1016/J.GEOMORPH.2017.03.031
Peruccacci S, Brunetti MT, Luciani S, Vennari C, Guzzetti F (2012) Lithological and seasonal control on rainfall thresholds for the possible initiation of landslides in central Italy. Geomorphology 139–140:79–90. https://doi.org/10.1016/J.GEOMORPH.2011.10.005
Piciullo L, Capobianco V, Heyerdahl H (2022) A first step towards a IoT-based local early warning system for an unsaturated slope in Norway, natural hazards. Springer, Netherlands. https://doi.org/10.1007/s11069-022-05524-3
Piciullo L, Gariano SL, Melillo M, Brunetti MT, Peruccacci S, Guzzetti F, Calvello M (2017) Definition and performance of a threshold-based regional early warning model for rainfall-induced landslides. Landslides 14:995–1008. https://doi.org/10.1007/s10346-016-0750-2
Pota M, Pecoraro G, Rianna G, Reder A, Calvello M, Esposito M (2022) Machine learning for the definition of landslide alert models: a case study in Campania region, Italy. Discov Artif Intell 2. https://doi.org/10.1007/s44163-022-00033-5
Reder A, Rianna G (2021) Exploring ERA5 reanalysis potentialities for supporting landslide investigations: a test case from Campania region (Southern Italy). Landslides 18:1909–1924. https://doi.org/10.1007/s10346-020-01610-4
Rosi A, Segoni S, Canavesi V, Monni A, Gallucci A, Casagli N (2021) Definition of 3D rainfall thresholds to increase operative landslide early warning system performances. Landslides 18:1045–1057. https://doi.org/10.1007/s10346-020-01523-2
Segoni S, Piciullo L, Gariano SL (2018) A review of the recent literature on rainfall thresholds for landslide occurrence. Landslides 22:478–494. https://doi.org/10.1007/s10346-018-0966-4
Sharifi E, Eitzinger J, Dorigo W (2019) Performance of the state-of-the-art gridded precipitation products over mountainous terrain: a regional study over Austria. Remote Sens 11:1–20. https://doi.org/10.3390/rs11172018
Steger S, Moreno M, Crespi A, Zellner PJ, Gariano SL, Brunetti T, Melillo M, Peruccacci S, Marra F, Kohrs R, Goetz J, Mair V, Pittore M (2022) Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models 1–38
UNDRR (2020) UN office for disaster risk reduction - human cost of disasters - an overview of the last 20 years 2000–2019
Wicki A, Jansson PE, Lehmann P, Hauck C, Stähli M (2021) Simulated or measured soil moisture: which one is adding more value to regional landslide early warning? Hydrol Earth Syst Sci 25:4585–4610. https://doi.org/10.5194/hess-25-4585-2021
Wicki A, Lehmann P, Hauck C, Seneviratne SI, Waldner P, Stähli M (2020) Assessing the potential of soil moisture measurements for regional landslide early warning. Landslides 17:1881–1896. https://doi.org/10.1007/s10346-020-01400-y
Xu J, Ma Z, Yan S, Peng J (2022) Do ERA5 and ERA5-land precipitation estimates outperform satellite-based precipitation products? A comprehensive comparison between state-of-the-art model-based and satellite-based precipitation products over mainland China. J Hydrol 605:127353. https://doi.org/10.1016/j.jhydrol.2021.127353
Acknowledgements
Pierpaolo Distefano expresses his sincere gratitude to NGI members, especially Natural hazards department, where he had the privilege of being a PhD visiting student for a six-months period. Their support, guidance, and collaboration have contributed significantly to his academic and personal growth.
Funding
Pierpaolo Distefano’s doctoral program is funded by the “Notice 2/2019 for financing the PhD regional grant in Sicily” as part of the Operational Programme of European Social Funding 2014–2020 (PO FSE 2014–2020, CUP E65E19000830002). Nunziarita Palazzolo is supported by a post-doctoral program funded by the project “Autorità di Bacino del Distretto Idrografico della Sicilia—Interventi per il miglioramento dei corpi idrici CUP: F62G16000000001.” This research was partially carried out within the projects HydrEx—Hydrological extremes in a changing climate— and VARIO—VAlutazione del Rischio Idraulico in sistemi cOmplessi—Piano di incentivi per la ricerca di Ateneo (Pia.ce.ri.), 2020–2022, Università di Catania, and the Ministero dell’Università e della Ricerca (Programma Operativo Nazionale Ricerca e Innovazione 2014–2020—Progetto “reCITY—Resilient City Everyday Revolution”—grant agreement no. ARS01_00592—CUP B69C21000390005). Pierpaolo Distefano’s post-doc is also funded by the project “reCITY—Resilient City—Everyday Revolution.”
Author information
Authors and Affiliations
Contributions
Conceptualization was done by DJP and LP; formal analysis by PD, LP, NP, and DJP; investigation by PD, LP, and DJP; methodology by PD, LP, and DJP; coding and mapping by PD, NP, and DJP; supervision by LP, DJP, PS, and AC; writing the original draft by PD and DJP; and the writing, review, and editing by PD, DJP, LP, and NP. DJP, LP, PS, and AC supervised the research. All authors have read and agreed to the published version of the paper.
Corresponding author
Ethics declarations
Competing interests
All authors declare no competing interests.
Supplementary information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Distefano, P., Peres, D.J., Piciullo, L. et al. Hydro-meteorological landslide triggering thresholds based on artificial neural networks using observed precipitation and ERA5-Land soil moisture. Landslides 20, 2725–2739 (2023). https://doi.org/10.1007/s10346-023-02132-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10346-023-02132-5