Open Access
Issue
E3S Web Conf.
Volume 699, 2026
11th International Conference on Energy and City of the Future (EVF’2024)
Article Number 03005
Number of page(s) 10
Section Water Management
DOI https://doi.org/10.1051/e3sconf/202669903005
Published online 20 March 2026
  1. S.E. Benziouche, F. Cheriet, Structure et contraintes de la filière dattes en Algérie, New Medit 11, 49 (2012). [Google Scholar]
  2. F. Sahnoune, M. Belhamel, M. Zelmat, R. Kerbachi, Climate change in algeria: Vulnerability and strategy of mitigation and adaptation, Energy Procedia 36, 1286 (2013). 10.1016/j.egypro.2013.07.145 [Google Scholar]
  3. B. Snellen, T. van Hattum, Soil moisture-based irrigation (2012), sTOWA Delta Facts (factsheet produced by Alterra). Originally produced December 2011; updated September 2012, https://www.stowa.nl/deltafacts/zoetwatervoorziening/delta-facts-english-versions/soil-moisture-based-irrigation [Google Scholar]
  4. H. Werner, Tech. Rep. FS876, South Dakota State University Cooperative Extension Service (2002), originally published July 1992; updated April 2002, http://agbiopubs.sdstate.edu/articles/FS876.pdf [Google Scholar]
  5. Manx Technology Group, Soil monitoring with IoT – smart agriculture, https://manxtechgroup.com/soil-monitoring-with-iot-smart-agriculture/ [Google Scholar]
  6. A. Rani, N. Kumar, J. Kumar, J. Kumar, N.K. Sinha, in Deep Learning for Sustainable Agriculture (Elsevier, 2022), Cognitive Data Science in Sustainable Computing, pp. 143–168, chapter 6, https://doi.org/10.1016/B978-0-323-85214-2.00001-X [Google Scholar]
  7. A. Uthayakumar, M.P. Mohan, E.H. Khoo, J. Jimeno, M.Y. Siyal, M.F. Karim, Machine learning models for enhanced estimation of soil moisture using wideband radar sensor, Sensors 22, 5810 (2022). 10.3390/s22155810 [Google Scholar]
  8. A. Singh, K. Gaurav, Deep learning and data fusion to estimate surface soil moisture from multi-sensor satellite images, Scientific Reports 13, 2251 (2023). 10.1038/s41598-023-28939-9 [Google Scholar]
  9. S. Prakash, A. Sharma, S.S. Sahu, Soil Moisture Prediction Using Machine Learning, in 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) (IEEE, Coimbatore, India, 2018), pp. 1–6, https://doi.org/10.1109/ICICCT.2018.8473260 [Google Scholar]
  10. H. Adab, R. Morbidelli, C. Saltalippi, M. Moradian, G.A. Fallah Ghalhari, Machine learning to estimate surface soil moisture from remote sensing data, Water 12, 3223 (2020). 10.3390/w12113223 [Google Scholar]
  11. Evidently AI, Accuracy vs. precision vs. recall in machine learning: what’s the difference?, https://www.evidentlyai.com/classification-metrics/accuracy-precision-recall [Google Scholar]
  12. ScienceDirect Topics, Root mean square error – an overview, https://www.sciencedirect.com/topics/engineering/root-mean-square-error [Google Scholar]
  13. Encyclopædia Britannica, Mean squared error (mse), https://www.britannica.com/science/mean-squared-error [Google Scholar]
  14. Pranjal, Mse vs mae: How to compare machine learning metrics, linkedIn Advice article, https://www.linkedin.com/advice/0/what-difference-between-mean-squared-error-tz1mc [Google Scholar]
  15. GeeksforGeeks, R-squared in regression analysis in machine learning, https://www.geeksforgeeks.org/ml-r-squared-in-regression-analysis/ [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.