Nonlinear Partial Least Squares for Consistency Analysis of Meteorological Data
Joint Authors
Meng, Zhen
Zhang, Shichang
Yang, Yan
Liu, Ming
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-04
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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 variables of the independent variables and the dependent variables are extracted by the partial least squares (PLS), and then they are, respectively, used as the inputs and outputs of neural network to build the nonlinear internal model of PLS.
The proposed method can deal with the limitation of traditional nonlinear PLS whose inner model is the fixed quadratic function or the spline function.
Two typical neural networks are used in the proposed method, and they are the back propagation neural network and the adaptive neuro-fuzzy inference system (ANFIS).
Moreover, the experiments are performed on the real data from the atmospheric observation equipment operation monitoring system of Shaanxi Province of China.
The experimental results verify that the nonlinear PLS with the internal model of ANFIS has higher effectiveness and could realize the consistency analysis of meteorological data correctly.
American Psychological Association (APA)
Meng, Zhen& Zhang, Shichang& Yang, Yan& Liu, Ming. 2015. Nonlinear Partial Least Squares for Consistency Analysis of Meteorological Data. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1072991
Modern Language Association (MLA)
Meng, Zhen…[et al.]. Nonlinear Partial Least Squares for Consistency Analysis of Meteorological Data. Mathematical Problems in Engineering No. 2015 (2015), pp.1-8.
https://search.emarefa.net/detail/BIM-1072991
American Medical Association (AMA)
Meng, Zhen& Zhang, Shichang& Yang, Yan& Liu, Ming. Nonlinear Partial Least Squares for Consistency Analysis of Meteorological Data. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1072991
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1072991