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A novel approach for detecting error measurements in a network of automatic weather stations

Abstract : 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.
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https://hal-univ-perp.archives-ouvertes.fr/hal-03527927
Contributor : Samira EL YACOUBI Connect in order to contact the contributor
Submitted on : Sunday, January 16, 2022 - 11:22:42 PM
Last modification on : Friday, August 5, 2022 - 2:44:08 PM

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R. Llugsi, Samira El Yacoubi, A. Fontaine, P. Lupera. A novel approach for detecting error measurements in a network of automatic weather stations. International Journal of Parallel, Emergent and Distributed Systems, Taylor & Francis, 2022, pp.1-18. ⟨10.1080/17445760.2021.2022672⟩. ⟨hal-03527927⟩

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