Articles | Volume 24, issue 3
https://doi.org/10.5194/nhess-24-823-2024
https://doi.org/10.5194/nhess-24-823-2024
Research article
 | 
08 Mar 2024
Research article |  | 08 Mar 2024

Space–time landslide hazard modeling via Ensemble Neural Networks

Ashok Dahal, Hakan Tanyas, Cees van Westen, Mark van der Meijde, Paul Martin Mai, Raphaël Huser, and Luigi Lombardo

Related authors

On crustal composition of the Sardinia-Corsica continental block inferred from receiver functions
Fabio Cammarano, Henrique Berger Roisenberg, Alessio Conclave, Islam Fadel, and Mark van der Meijde
EGUsphere, https://doi.org/10.5194/egusphere-2024-1515,https://doi.org/10.5194/egusphere-2024-1515, 2024
Short summary
Multi-hazard susceptibility mapping of cryospheric hazards in a high-Arctic environment: Svalbard Archipelago
Ionut Cristi Nicu, Letizia Elia, Lena Rubensdotter, Hakan Tanyaş, and Luigi Lombardo
Earth Syst. Sci. Data, 15, 447–464, https://doi.org/10.5194/essd-15-447-2023,https://doi.org/10.5194/essd-15-447-2023, 2023
Short summary
Physically based modeling of co-seismic landslide, debris flow, and flood cascade
Bastian van den Bout, Chenxiao Tang, Cees van Westen, and Victor Jetten
Nat. Hazards Earth Syst. Sci., 22, 3183–3209, https://doi.org/10.5194/nhess-22-3183-2022,https://doi.org/10.5194/nhess-22-3183-2022, 2022
Short summary
Insights from the topographic characteristics of a large global catalog of rainfall-induced landslide event inventories
Robert Emberson, Dalia B. Kirschbaum, Pukar Amatya, Hakan Tanyas, and Odin Marc
Nat. Hazards Earth Syst. Sci., 22, 1129–1149, https://doi.org/10.5194/nhess-22-1129-2022,https://doi.org/10.5194/nhess-22-1129-2022, 2022
Short summary
Spatiotemporal clustering of flash floods in a changing climate (China, 1950–2015)
Nan Wang, Luigi Lombardo, Marj Tonini, Weiming Cheng, Liang Guo, and Junnan Xiong
Nat. Hazards Earth Syst. Sci., 21, 2109–2124, https://doi.org/10.5194/nhess-21-2109-2021,https://doi.org/10.5194/nhess-21-2109-2021, 2021
Short summary

Related subject area

Landslides and Debris Flows Hazards
Size scaling of large landslides from incomplete inventories
Oliver Korup, Lisa V. Luna, and Joaquin V. Ferrer
Nat. Hazards Earth Syst. Sci., 24, 3815–3832, https://doi.org/10.5194/nhess-24-3815-2024,https://doi.org/10.5194/nhess-24-3815-2024, 2024
Short summary
InSAR-informed in situ monitoring for deep-seated landslides: insights from El Forn (Andorra)
Rachael Lau, Carolina Seguí, Tyler Waterman, Nathaniel Chaney, and Manolis Veveakis
Nat. Hazards Earth Syst. Sci., 24, 3651–3661, https://doi.org/10.5194/nhess-24-3651-2024,https://doi.org/10.5194/nhess-24-3651-2024, 2024
Short summary
A coupled hydrological and hydrodynamic modeling approach for estimating rainfall thresholds of debris-flow occurrence
Zhen Lei Wei, Yue Quan Shang, Qiu Hua Liang, and Xi Lin Xia
Nat. Hazards Earth Syst. Sci., 24, 3357–3379, https://doi.org/10.5194/nhess-24-3357-2024,https://doi.org/10.5194/nhess-24-3357-2024, 2024
Short summary
More than one landslide per road kilometer – surveying and modeling mass movements along the Rishikesh–Joshimath (NH-7) highway, Uttarakhand, India
Jürgen Mey, Ravi Kumar Guntu, Alexander Plakias, Igo Silva de Almeida, and Wolfgang Schwanghart
Nat. Hazards Earth Syst. Sci., 24, 3207–3223, https://doi.org/10.5194/nhess-24-3207-2024,https://doi.org/10.5194/nhess-24-3207-2024, 2024
Short summary
Temporal clustering of precipitation for detection of potential landslides
Fabiola Banfi, Emanuele Bevacqua, Pauline Rivoire, Sérgio C. Oliveira, Joaquim G. Pinto, Alexandre M. Ramos, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 24, 2689–2704, https://doi.org/10.5194/nhess-24-2689-2024,https://doi.org/10.5194/nhess-24-2689-2024, 2024
Short summary

Cited articles

Abraham, N. and Khan, N. M.: A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation, CoRR, abs/1810.07842, arXiv [preprint], https://doi.org/10.48550/arXiv.1810.07842, 2018. a, b
Aguilera, Q., Lombardo, L., Tanyas, H., and Lipani, A.: On The Prediction of Landslide Occurrences and Sizes via Hierarchical Neural Networks, Stoch. Env. Res. Risk A., 36, 2031–2048, 2022. a, b
Alvioli, M., Marchesini, I., Reichenbach, P., Rossi, M., Ardizzone, F., Fiorucci, F., and Guzzetti, F.: Automatic delineation of geomorphological slope units with <tt>r.slopeunits v1.0</tt> and their optimization for landslide susceptibility modeling, Geosci. Model Dev., 9, 3975–3991, https://doi.org/10.5194/gmd-9-3975-2016, 2016. a
Amit, S. N. K. B. and Aoki, Y.: Disaster detection from aerial imagery with convolutional neural network, in: 2017 international electronics symposium on knowledge creation and intelligent computing (IES-KCIC), Surabaya, Indonesia, 26–27 September, IEEE, 239–245, https://doi.org/10.1109/KCIC.2017.8228593, 2017. a
Ardizzone, F., Cardinali, M., Carrara, A., Guzzetti, F., and Reichenbach, P.: Impact of mapping errors on the reliability of landslide hazard maps, Nat. Hazards Earth Syst. Sci., 2, 3–14, https://doi.org/10.5194/nhess-2-3-2002, 2002. a
Download
Short summary
We propose a modeling approach capable of recognizing slopes that may generate landslides, as well as how large these mass movements may be. This protocol is implemented, tested, and validated with data that change in both space and time via an Ensemble Neural Network architecture.
Altmetrics
Final-revised paper
Preprint