Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/73847
Título: Estimation of wind intensity data from reanalysis data using a shallow neural network
Autores/as: Rodríguez-Esparragón, Dionisio 
Marcello, Javier 
Marrero Betancort, Nerea 
Gonzalo-Martin, Consuelo 
Clasificación UNESCO: 2502 Climatología
Palabras clave: Global Change
Prediction
Shallow Neural Net
SNN
Wind Intensity
Fecha de publicación: 2019
Conferencia: 2019 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2019 
Resumen: Global change is one of the outstanding problems nowadays. This is the reason why considerable attention, and economic resources to monitor climate variables have increased. Wind data constitute one of the key elements that determine the local climate. In this paper, the performance of a shallow neural net (SNN) is tested to simulate remote sensing wind intensity data from reanalysis data from nearby location. As a result, a sequence of wind data with more spatial resolution can be achieved, allowing the availability of more data at the local scale.
URI: http://hdl.handle.net/10553/73847
ISBN: 9781728109671
DOI: 10.1109/IWOBI47054.2019.9114455
Fuente: IWOBI 2019 - IEEE International Work Conference on Bioinspired Intelligence, Proceedings, p. 87-92, (Julio 2019)
Colección:Actas de congresos
Vista completa

Visitas

81
actualizado el 27-ene-2024

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.