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All issues Volume 367 (2026) EPJ Web Conf., 367 (2026) 03013 References
Open Access
Issue
EPJ Web Conf.
Volume 367, 2026
Fifth International Conference on Robotics, Intelligent Automation and Control Technologies (RIACT 2026)
Article Number 03013
Number of page(s) 20
Section Smart and Sustainable Systems
DOI https://doi.org/10.1051/epjconf/202636703013
Published online 29 April 2026
  1. R. Al-Ruzouq, A. Shanableh, R. Jena, S. Mukherjee, M.A. Khalil, M.B.A. Gibril, A. Pradhan, N.A. Hammouri, Hybrid deep learning and remote sensing for the delineation of artificial groundwater recharge zones. Egypt. J. Remote Sens. Space Sci. 27, 178–191 (2024). https://doi.org/10.1016/j.ejrs.2024.03.003 [Google Scholar]
  2. S. Mo, Y. Zhong, X. Shi, W. Feng, X. Yin, J. Wu, Improving prediction of terrestrial water storage anomalies during the GRACE and GRACE-FO gap using Bayesian convolutional neural networks. arXiv:2101.09361 (2021) [Google Scholar]
  3. F. Pech-May, R. Aquino-Santos, J. Delgadillo-Partida, Sentinel-1 SAR images and deep learning for water body mapping. Remote Sens. 15, 3009 (2023). https://doi.org/10.3390/rs15123009 [Google Scholar]
  4. R. Gharbia, Deep learning for automatic extraction of water bodies using satellite imagery. J. Indian Soc. Remote Sens. 51, 1511–1521 (2023). https://doi.org/10.1007/s12524-023-01684-7 [Google Scholar]
  5. Z. Al-Ali, A. Abulibdeh, T. Al-Awadhi, M. Mohan, N. Al Nasiri, M. Al-Barwani, S. Al Nabbi, M. Abdullah, Examining the potential and effectiveness of water indices using multispectral Sentinel-2 data to detect soil moisture as an indicator of mudflow occurrence in arid regions. Int. J. Appl. Earth Obs. Geoinf. 130, 103887 (2024). https://doi.org/10.1016/j.jag.2024.103887 [Google Scholar]
  6. A. Tiwari, M. Silver, A. Karnieli, A deep learning approach for automatic identification of ancient agricultural water harvesting systems. Int. J. Appl. Earth Obs. Geoinf. 118, 103270 (2023). https://doi.org/10.1016/j.jag.2023.103270 [Google Scholar]
  7. F.J. Peña, C. Hübinger, A.H. Payberah, F. Jaramillo, DeepAqua: self-supervised semantic segmentation of wetland surface water extent with SAR images using knowledge distillation. arXiv:2305.01698 (2023) [Google Scholar]
  8. H. Morgan, A. Madani, H.M. Hussien, T. Nassar, Using an ensemble machine learning model to delineate groundwater potential zones in desert fringes of East Esna-Idfu area, Nile Valley, Upper Egypt. Geosci. Lett. 10, 9 (2023). https://doi.org/10.1186/s40562-023-00262-6 [Google Scholar]
  9. W. Liu, H. Yu, L. Yang, Z. Yin, M. Zhu, X. Wen, Deep learning-based predictive framework for groundwater level forecast in arid irrigated areas. Water 13, 2558 (2021). https://doi.org/10.3390/w13182558 [Google Scholar]
  10. Y. Wang, C.M. Albrecht, N.A.A. Braham, L. Mou, X.X. Zhu, Self-supervised learning in remote sensing: a review. IEEE Geosci. Remote Sens. Mag. 10, 213–247 (2022). https://doi.org/10.1109/MGRS.2022.3214141 [Google Scholar]
  11. M. Attya, O.M. Abo-Seida, H.M. Abdulkader, A.M. Mohammed, A hybrid deep learning approach for accurate water body segmentation in satellite imagery. Earth Sci. Inform. 18, 418 (2025). https://doi.org/10.1007/s12145-025-01418-0 [Google Scholar]
  12. Y. Wang, C. Liu, A. Tiwari, M. Silver, A. Karnieli, X.X. Zhu, C.M. Albrecht, Deep semantic model fusion for ancient agricultural terrace detection. In Proc. IEEE Int. Conf. Big Data, 4888–4892 (2022). https://doi.org/10.1109/BigData55660.2022.10020814 [Google Scholar]
  13. F.H. Wagner, S. Favrichon, R. Dalagnol, M.C. Hirye, A. Mullissa, S. Saatchi, The Amazon’s 2023 drought: Sentinel-1 reveals extreme Rio Negro river contraction. Remote Sens. 16, 1056 (2024). https://doi.org/10.3390/rs16061056 [Google Scholar]
  14. H. Sahour, M. Sultan, B. Abdellatif, M. Emil, A.Z. Abotalib, K. Abdelmohsen, M. Vazifedan, A.T. Mohammad, S.M. Hassan, M.R. Metwalli, M. El Bastawesy, Identification of shallow groundwater in arid lands using multi-sensor remote sensing data and machine learning algorithms. J. Hydrol. 614, 128509 (2022). https://doi.org/10.1016/j.jhydrol.2022.128509 [Google Scholar]
  15. M. Kalaiyarasi, S. Saravanan, S. Karthi, K. Karunanithi, P. Priya, Water body area identification and classification using hyperspectral images. In Proc. Int. Conf. Computing in Engineering & Technology, 89–91 (2022). https://doi.org/10.1049/icp.2022.0891 [Google Scholar]
  16. Z. Pang, Z. Zhou, J.E. Fu, W. Jiang, X. Qin, M. Sun, Deep learning-based remote sensing retrieval of inland water quality: a review. J. Hydrol. Reg. Stud. 61, 102759 (2025). https://doi.org/10.1016/j.ejrh.2025.102759 [Google Scholar]
  17. D. Pan, Y. Deng, S.X. Yang, B. Gharabaghi, Recent advances in remote sensing and artificial intelligence for river water quality forecasting: a review. Environments 12, 158 (2025). https://doi.org/10.3390/environments12050158 [Google Scholar]
  18. M. Tesfaye, L. Breuer, Remote sensing with machine learning for multi-decadal surface water monitoring in Ethiopia. Sci. Rep. 15, 12444 (2025). https://doi.org/10.1038/s41598-025-97244-3 [Google Scholar]
  19. Z. Dai, C. Zhan, H. Yin, J. Chen, L. Xu, Y. Xia, S. Yang, W. Chen, M. Cao, Z. Du, X. Zhang, Incorporating deep learning into hydrogeological modeling: advancements, challenges, and future directions. J. Geophys. Res. Mach. Learn. Comput. 2, e2025JH000703 (2025). https://doi.org/10.1029/2025JH000703 [Google Scholar]
  20. L. Rodríguez-López, L. Bravo Alvarez, I. Duran-Llacer, D.E. Ruíz-Guirola, S. Montejo-Sánchez, R. Martínez-Retureta, E. López-Morales, L. Bourrel, F. Frappart, R. Urrutia, Leveraging machine learning and remote sensing for water quality analysis in Lake Ranco, southern Chile. Remote Sens. 16, 3401 (2024). https://doi.org/10.3390/rs16183401 [Google Scholar]
  21. A. Elmotawakkil, A. Moumane, A. Zahi, A. Sadiki, J. Al Karkouri, M. Batchi, S.K. Bhagat, T. Tiyasha, N. Enneya, Artificial intelligence for groundwater recharge prediction in an arid region: application of tabular deep learning models in the Feija Basin, Morocco. Front. Remote Sens. 6, 1622360 (2025). https://doi.org/10.3389/frsen.2025.1622360 [Google Scholar]
  22. W. Zhi, A.P. Appling, H.E. Golden, J. Podgorski, L. Li, Deep learning for water quality. Nat. Water 2, 228–241 (2024). https://doi.org/10.1038/s44221-024-00207-0 [Google Scholar]
  23. Y.I. Kim, W.H. Park, Y. Shin, J.W. Park, B. Engel, Y.J. Yun, W.S. Jang, Applications of machine learning and remote sensing in soil and water conservation. Hydrology 11, 183 (2024). https://doi.org/10.3390/hydrology11110183 [Google Scholar]
  24. M. Sit, B.Z. Demiray, Z. Xiang, G.J. Ewing, Y. Sermet, I. Demir, A comprehensive review of deep learning applications in hydrology and water resources. Water Sci. Technol. 82, 2635–2670 (2020). https://doi.org/10.2166/wst.2020.369 [Google Scholar]

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