A novel approach for detecting error measurements in a network of automatic weather stations. Issue 4 (4th July 2022)

• Record Type: Journal Article
• Authors: Llugsi, R.; El Yacoubi, S.; Fontaine, A.; Lupera, P.
• Is Part Of: International journal of parallel, emergent and distributed systems. Volume 37:Issue 4(2022)
• Page Start: 425
• Page End: 442
• Publication Date: 2022-07-04
• Subjects: LSTM -- time series -- convolutional -- encoder–decoder -- neural network -- walk-forward validation -- dropout -- Bayesian uncertainty; Parallel computers -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Computer algorithms -- Periodicals; 004.35
• DOI: 10.1080/17445760.2021.2022672
• Languages: English
• ISSNs: 1744-5760

Abstract

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. GRAPHICAL ABSTRACT: UF0001