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Article Dans Une Revue International Journal of Parallel, Emergent and Distributed Systems Année : 2022

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

Résumé

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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Dates et versions

hal-04053030 , version 1 (30-03-2023)

Identifiants

Citer

R. Llugsi, Samira El Yacoubi, Allyx Fontaine, Pablo Lupera. A novel approach for detecting error measurements in a network of automatic weather stations. International Journal of Parallel, Emergent and Distributed Systems, 2022, 37 (4), pp.425-442. ⟨10.1080/17445760.2021.2022672⟩. ⟨hal-04053030⟩
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