| Autor: |
Llugsi, R., El Yacoubi, S., Fontaine, A., Lupera, P. |
| Předmět: |
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| Zdroj: |
International Journal of Parallel, Emergent & Distributed Systems; Aug2022, Vol. 37 Issue 4, p425-442, 18p |
| Abstrakt: |
In the present work, a novel methodology for error detection in automatic weather stations has been implemented. Time series acquired from two highly correlated stations with a station under analysis are utilised to obtain a 24-h air temperature forecast that allows to know if a station register erroneous measurements. Four models to obtain a reliable forecast have been analysed, auto-regressive integrated moving average, Long Short-Term Memory (LSTM), LSTM stacked and a convolutional LSTM model with uncertainty error reduction. The analysis carried out exhibits a significant success with the methodology for three stations reaching error values between 0.98 ∘ C and 1.50 ∘ C and correlation coefficients between 0.72 and 0.81. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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