Communication Dans Un Congrès Année : 2020

Multilayered Ensemble Learning for short-term forecasting in agro-climatology

Résumé

Agriculture is one of the areas whose activities depend heavily on weather forecasts. Indeed, in order to optimize their production, farmers must be able to anticipate climate conditions favorable or not to their activities by deploying the appropriate action plans. For this purpose, they consult the data daily from various suppliers of weather forecasts. However, the reliability of the forecasts of each supplier is variable according to the period, the climate or the geographical area. Farmers, therefore, have to arbitrate between suppliers daily. This paper proposes a new set of learning architecture that significantly improves the accuracy of weather short-term forecasts for the next 1-12h in order to assist farmers in decision-making.

Fichier principal
Vignette du fichier
ICCTA_IDEAC__Copy_ (1).pdf (696.86 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

hal-03151748 , version 1 (26-07-2022)

Licence

Identifiants

Citer

Jade Guisiano, Raja Chiky, Julien Orensanz, Shohreh Ahvar. Multilayered Ensemble Learning for short-term forecasting in agro-climatology. 6th International Conference on Computer and Technology Applications, 2020, Antalya, Turkey. ⟨10.1145/3397125.3397130⟩. ⟨hal-03151748⟩

Collections

82 Consultations
186 Téléchargements

Altmetric

Partager

  • More