2015
DOI: 10.1155/2015/143965
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Nonlinear Partial Least Squares for Consistency Analysis of Meteorological Data

Abstract: Considering the different types of error and the nonlinearity of the meteorological measurement, this paper proposes a nonlinear partial least squares method for consistency analysis of meteorological data. For a meteorological element from one automated weather station, the proposed method builds the prediction model based on the corresponding meteorological elements of other surrounding automated weather stations to determine the abnormality of the measured values. For the proposed method, the latent variabl… Show more

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Cited by 2 publications
(3 citation statements)
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“…PLS/NPLS algorithms have been applied in engineering (for example, see [12][13][14]). However, due to the limited number of samples, the prediction accuracy of PLS/NPLS based on traditional k-fold cross validation may decrease.…”
Section: Estimations Of Nox and Reheat Steam Temperature Based On Mo-mentioning
confidence: 99%
“…PLS/NPLS algorithms have been applied in engineering (for example, see [12][13][14]). However, due to the limited number of samples, the prediction accuracy of PLS/NPLS based on traditional k-fold cross validation may decrease.…”
Section: Estimations Of Nox and Reheat Steam Temperature Based On Mo-mentioning
confidence: 99%
“…(1) Run the BACON algorithm on the dataset using distance (6), and keep the outcome . Then delete the observations in the dependent variable related to the outliers to obtain (free from outliers).…”
Section: Robust Nonlinear Plsmentioning
confidence: 99%
“…Wold [5] also proposed the spline PLS algorithm. Another nonlinear algorithm based on neural networks to deal with the nonlinearity of meteorological data was proposed [6].…”
Section: Introductionmentioning
confidence: 99%