ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 41 Issue 8 > 2015,41(8):964-969. DOI:10.7519/j.issn.1000-0526.2015.08.005 Prev Next
Automated Observation Model for Frost Based onBayes Discriminant Method
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Abstract:
The correlations between frost and temperature, surface temperature, vapor pressure, wind speed and other meteorological factors are discussed in this paper by using the observation data from Anhui Dangshan Weather Station in the winter halfyear (from October to April of the next year) from 2001 to 2013. Using stepwise discriminant analysis method, multiple sets of frost automatic discriminant models for the occurrence of frost are built based on Bayes discriminant method. The results show that: (1) The occurrence of frost is significantly correlated with daily minimum temperature, night temperature of different observation time and surface temperature. The lower the night temperature or the surface temperature is, the larger the possibility of the temperature is lower than the frost point and the greater the possibility of the frost occurrence. (2) Through the back calculation test and prediction test of independent samples, the average accuracy rate of unoccurred frost discriminated by the frost model is 86.5% based on Bayes discriminant method and the average accuracy rate of the seen frost is 92.7%. The three factor models based on the daily minimum temperature, the daily vapor pressure at 07:00 BT and the daily wind speed at 07:00 BT are optimal. The accuracy rate of discriminating the frost occurrence by the three factor models is more than 90%. Therefore, we can combine the Bayes frost discriminant model with image recognition technology, and apply the new technology to frost automatic observation.
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