Towards Improved Rainfall Forecast Within the Ziz Basin Area: A Focused Exploration of Machine Learning Application
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-63038-5_12
Reference19 articles.
1. Al Mamun A et al (2018) Estimation of short-duration rainfall intensity from daily rainfall values in Klang Valley, Malaysia. Appl Water Sci 8:203. https://doi.org/10.1007/s13201-018-0854-z
2. AlHassoun SA (2011) Developing an empirical formulae to estimate rainfall intensity in Riyadh region. J King Saud Univ Eng Sci 23:81–88. https://doi.org/10.1016/j.jksues.2011.03.003
3. Barrera-Animas AY et al (2022) Rainfall prediction: a comparative analysis of modern machine learning algorithms for time-series forecasting. Mach Learn Appl 7:100204. https://doi.org/10.1016/j.mlwa.2021.100204
4. Bouziane S et al (2021) Proposal of a big data system for an intelligent management of water resources. In: Proceedings of the 6th edition of SAWIS international conference on water management, applied computing and data science (WMAD21), E3S web conference, vol 314, p 02002. https://doi.org/10.1051/e3sconf/202131402002
5. Bouziane S et al (2022) The use of big data in water resources management. In: Proceedings of the 2nd international conference on big data, modelling and machine learning (BML). SciTePress, pp 38–44. https://doi.org/10.5220/0010728000003101
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