Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/73847
Title: Estimation of wind intensity data from reanalysis data using a shallow neural network
Authors: Rodríguez-Esparragón, Dionisio 
Marcello, Javier 
Marrero Betancort, Nerea 
Gonzalo-Martin, Consuelo 
UNESCO Clasification: 2502 Climatología
Keywords: Global Change
Prediction
Shallow Neural Net
SNN
Wind Intensity
Issue Date: 2019
Conference: 2019 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2019 
Abstract: 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
Source: IWOBI 2019 - IEEE International Work Conference on Bioinspired Intelligence, Proceedings, p. 87-92, (Julio 2019)
Appears in Collections:Actas de congresos
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