|Title:||Estimation of wind intensity data from reanalysis data using a shallow neural network||Authors:||Rodríguez-Esparragón, Dionisio
Marrero Betancort, Nerea
|UNESCO Clasification:||2502 Climatología||Keywords:||Global Change
Shallow Neural Net
|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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