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 |
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.