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

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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
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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.
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