Learning with deep Gaussian processes and homothety in weather simulation

Author:

Coulibaly Lassana,Kounta Cheick Abdoul Kadir A.,Kamsu-Foguem BernardORCID,Tangara Fana

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference34 articles.

1. Abaza M, Anctil F, Fortin V, Perreault L (2017) On the incidence of meteorological and hydrological processors: effect of resolution, sharpness and reliability of hydrological ensemble forecasts. J Hydrol 555:371–384

2. Abdelfatah K, Bao J, Terejanu G (2018) Geospatial uncertainty modeling using stacked Gaussian processes. Environ Model Softw 109:293–305

3. Agüera-Pérez A, Palomares-Salas JC, González de la Rosa JJ, Florencias-Oliveros O (2018) Weather forecasts for microgrid energy management: review, discussion and recommendations. Appl Energy 228:265–278

4. Chen J, Li K, Li K, Yu PS, Zeng Z (2021) Dynamic planning of bicycle stations in dockless public bicycle-sharing system using gated graph neural network. ACM Trans Intell Syst Technol (TIST) 12:1–22

5. Coulibaly L (2020) Contribution en apprentissage automatique pour la maîtrise des risques. Thesis dissertation, Toulouse University, Defense, 17 December 2020, Catalogue SUDOC, Thesis number 2020INPT0109

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