Mapping Subsidence Susceptibility and Risks in the Rhineland Coalfields: Leveraging EGMS Data and Machine Learning

Dibakar Kamalini Ritushree; Marzieh Baes; Maoqi Liu; Mahdi Motagh

doi:https://doi.org/10.5194/egusphere-egu25-13592

[Back] [Session NH3.8]

EGU25-13592, updated on 15 Mar 2025

https://doi.org/10.5194/egusphere-egu25-13592

EGU General Assembly 2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Mapping Subsidence Susceptibility and Risks in the Rhineland Coalfields: Leveraging EGMS Data and Machine Learning

Dibakar Kamalini Ritushree¹, Marzieh Baes¹, Maoqi Liu^1,2, and Mahdi Motagh

¹

Dibakar Kamalini Ritushree et al.

¹German Research Centre for Geosciences, Geodesy, Potsdam, Germany (dibakar@gfz-potsdam.de)
²School of Resources & Environment and Safety Engineering, Hunan University of Science and Technology, Xiangtan 411201, China

How to cite: Ritushree, D. K., Baes, M., Liu, M., and Motagh, M.: Mapping Subsidence Susceptibility and Risks in the Rhineland Coalfields: Leveraging EGMS Data and Machine Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13592, https://doi.org/10.5194/egusphere-egu25-13592, 2025.

This abstract has been withdrawn on 21 Apr 2025.