Machine-learning-based nowcasting of the Vögelsberg deep-seated landslide: why predicting slow deformation is not so easy

van Natijne, Adriaan L.; Bogaard, Thom A.; Zieher, Thomas; Pfeiffer, Jan; Lindenbergh, Roderik C.

doi:https://doi.org/10.5194/nhess-23-3723-2023

Articles | Volume 23, issue 12

https://doi.org/10.5194/nhess-23-3723-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Advances in machine learning for natural hazards risk...

https://doi.org/10.5194/nhess-23-3723-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 12

Research article

|

01 Dec 2023

Research article |

| 01 Dec 2023

Machine-learning-based nowcasting of the Vögelsberg deep-seated landslide: why predicting slow deformation is not so easy

Adriaan L. van Natijne, Thom A. Bogaard, Thomas Zieher, Jan Pfeiffer, and Roderik C. Lindenbergh

Data sets

ERA5-Land Hourly Data from 2001 to Present J. Muñoz Sabater https://doi.org/10.24381/CDS.E2161BAC

GPM IMERG Final Precipitation L3 Half Hourly 0.1 Degree x 0.1 Degree V06 G. Huffman, E. Stocker, D. Bolvin, E. Nelkin, and T. Jackson https://doi.org/10.5067/GPM/IMERG/3B-HH-E/06

SMAP L4 global 3-hourly 9 km EASE-grid surface and root zone soil moisture, version 6 R. Reichle, G. De Lannoy, R. D. Koster, W. T. Crow, J. S. Kimball, and Q. Liu https://doi.org/10.5067/08S1A6811J0U

Vögelsberg deformation time series Land Tirol, Department of Geoinformation https://www.tirol.gv.at/sicherheit/geoinformation/vermessung-monitoring/monitoring/

Global Land Evaporation Amsterdam Model (GLEAM) A. Koppa and D. Rains https://www.gleam.eu

Model code and software

TensorFlow TensorFlow Developers https://doi.org/10.5281/zenodo.4724125

Short summary

Landslides are one of the major weather-related geohazards. To assess their potential impact and design mitigation solutions, a detailed understanding of the slope is required. We tested if the use of machine learning, combined with satellite remote sensing data, would allow us to forecast deformation. Our results on the Vögelsberg landslide, a deep-seated landslide near Innsbruck, Austria, show that the formulation of such a machine learning system is not as straightforward as often hoped for.