Research on Data Mining Algorithm of Meteorological Observation Based on Data Quality Control Algorithm

The quality of meteorological observation data directly affects the weather forecast and the accuracy of climate prediction. The traditional quality control algorithm is not sensitive to the abnormal changes of the elements and can’t meet the needs of the quality control work. Therefore, based on th...

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Bibliographic Details
Published in:Wireless personal communications 2018-09, Vol.102 (2), p.2077-2089
Main Authors: Qing-Dao-Er-Ji, Ren, Li, Na
Format: Article
Language:English
Subjects:
Algorithms
Communications Engineering
Computer Communication Networks
Computer simulation
Control algorithms
Control methods
Control theory
Correlation
Data mining
Engineering
Networks
Neural networks
Quality control
Signal,Image and Speech Processing
Support vector machines
Weather forecasting
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Summary:The quality of meteorological observation data directly affects the weather forecast and the accuracy of climate prediction. The traditional quality control algorithm is not sensitive to the abnormal changes of the elements and can’t meet the needs of the quality control work. Therefore, based on the data mining algorithm, this paper further studied the quality control of meteorological data from two aspects of time correlation and factor correlation. Two different methods of quality control for meteorological observation data were proposed. One is the quality control method of time correlated meteorological observations based on the characteristics of chaos (potential trend and regularity) and the support vector machine algorithm. The other is the quality control method of factor correlated meteorological observations based on BP neural network and the characteristics of different elements. Combining the complementarity and relevance between the two methods, a set of comprehensive quality control scheme is set up. The experimental results show that the proposed scheme can effectively simulate the weather observation data and detect the anomaly value.
ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-018-5258-8