Water table rise forecasting using machine and deep learning models in arid regions, Oman

Hussam Eldin Elzain; Osman Abdalla; Ali Al-Maktoumi; Anvar Kacimov; Mingjie Chen

doi:https://doi.org/10.5194/egusphere-egu25-12464

[Back] [Session ITS1.4/CL0.10]

EGU25-12464, updated on 15 Mar 2025

https://doi.org/10.5194/egusphere-egu25-12464

EGU General Assembly 2025

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

Water table rise forecasting using machine and deep learning models in arid regions, Oman

Hussam Eldin Elzain

¹, Osman Abdalla², Ali Al-Maktoumi^1,3, Anvar Kacimov³, and Mingjie Chen¹

Hussam Eldin Elzain et al.

¹Water Research Center, Sultan Qaboos University, Muscat, Oman (halzain944@gmail.com)
²Department of Earth Sciences, College of Science, Sultan Qaboos University, Oman
³Department of Soils, Water and Agricultural Engineering, Sultan Qaboos University, Oman

How to cite: Elzain, H. E., Abdalla, O., Al-Maktoumi, A., Kacimov, A., and Chen, M.: Water table rise forecasting using machine and deep learning models in arid regions, Oman, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12464, https://doi.org/10.5194/egusphere-egu25-12464, 2025.

This abstract has been withdrawn on 25 Jul 2025.