Open Access
| Issue |
BIO Web Conf.
Volume 211, 2026
International Conference on Water Resources and Environmental Studies (ICWES 2025)
|
|
|---|---|---|
| Article Number | 01012 | |
| Number of page(s) | 16 | |
| DOI | https://doi.org/10.1051/bioconf/202621101012 | |
| Published online | 15 January 2026 | |
- I. Ouchkir et al., « Spatio-Temporal Climate Variability in Beni Mellal-Khenifra Region (Morocco) », The International Journal of Climate Change: Impacts and Responses, vol. 17, no 2, p. 1–26, 2025, doi: 10.18848/1835-7156/cgp/v17i02/1-26. [Google Scholar]
- A. Boundi et Z. Ait Yacine, « Characterization of climate impacts on a semi-arid agricultural perimeter in Morocco », in E3S Web of Conferences, EDP Sciences, 2022, p. 02001. [Google Scholar]
- C. Asopa, « The Role of Sensor in Environmental Monitoring », Tuijin Jishu/Journal of Propulsion Technology, vol. 43, p. 259–262, nov. 2023. doi: 10.52783/tjjpt.v43.i4.2348. [Google Scholar]
- S. S. Shafin, G. Karmakar, I. Mareels, V. Balasubramanian, et R. R. Kolluri, « Whose Data are Reliable: Sensor Declared Data Reliability », in 2023 19th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), juin 2023, p. 249–254. doi: 10.1109/WiMob58348.2023.10187811. [Google Scholar]
- R. M. Balabin et E. I. Lomakina, « Support vector machine regression (SVR/LS-SVM)—an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data », Analyst, vol. 136, no 8, p. 1703–1712, 2011, Consulté le: 28 août 2025. [En ligne]. Disponible sur: https://pubs.rsc.org/en/content/articlehtml/2011/an/c0an00387e [Google Scholar]
- M. Al-Mukhtar, « Random forest, support vector machine, and neural networks to modelling suspended sediment in Tigris River-Baghdad », Environ Monit Assess, vol. 191, no 11, p. 673, oct. 2019, doi: 10.1007/s10661-019-7821-5. [Google Scholar]
- A. Malik, Y. Tikhamarine, D. Souag-Gamane, S. S. Sammen, et O. Kisi, « Support vector regression model optimized with GWO versus GA algorithms: Estimating daily pan-evaporation », in Handbook of Hydroinformatics, S. Eslamian et F. Eslamian, Éd., Elsevier, 2023, p. 357–373. doi: 10.1016/B978-0-12-821961-4.00001-4. [Google Scholar]
- L. Breiman, « Random Forests », Machine Learning, vol. 45, no 1, p. 5–32, oct. 2001, doi: 10.1023/A:1010933404324. [Google Scholar]
- M. M. Hameed et al., « Application of Artificial Intelligence Models for Evapotranspiration Prediction along the Southern Coast of Turkey », Complexity, vol. 2021, no 1, janv. 2021, doi: 10.1155/2021/8850243. [Google Scholar]
- H. K. Al-Mahdawi et al., « Analysis and prediction of evaporation rates using random forest models: a case study of Almaty city », Babylonian Journal of Machine Learning, vol. 2023, p. 55–64, 2023, Consulté le: 28 août 2025. [En ligne]. Disponible sur: https://mesopotamian.press/journals/index.php/BJML/article/view/467 [Google Scholar]
- H. A. Salman, A. Kalakech, et A. Steiti, « Random forest algorithm overview », Babylonian Journal of Machine Learning, vol. 2024, p. 69–79, 2024, Consulté le: 30 août 2025. [En ligne]. Disponible sur: https://journals.mesopotamian.press/index.php/BJML/article/view/417 [Google Scholar]
- A. Vargo, F. Zhang, M. Yurochkin, et Y. Sun, « Individually Fair Gradient Boosting », 31 mars 2021, arXiv: arXiv:2103.16785. doi: 10.48550/arXiv.2103.16785. [Google Scholar]
- Z. A. Al Sudani et G. S. A. Salem, « Evaporation Rate Prediction Using Advanced Machine Learning Models: A Comparative Study », Advances in Meteorology, vol. 2022, no 1, p. 1433835, 2022, doi: 10.1155/2022/1433835. [Google Scholar]
- V. DePuy, V. W. Berger, et Y. Zhou, « Wilcoxon-Mann-Whitney Test: Overview », in Wiley StatsRef: Statistics Reference Online, John Wiley & Sons, Ltd, 2014. doi: 10.1002/9781118445112.stat06547. [Google Scholar]
- G. I. Salov, « On the power of a new statistical test and two-sample Wilcoxon test », Optoelectron.Instrument.Proc., vol. 50, no 1, p. 36–48, janv. 2014. doi: 10.3103/S8756699014010051. [Google Scholar]
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