Abstract
Based on field experiments at Nanyue Mountain Meteorological Station and Huaihua National Reference Climatological Station in Hunan Province, the camera images of icing weather phenomena, such as glaze, rime and mixing rime, are collected minutely from January to March in 2018. The convolution neural network technology is employed for modelling and training using the camera images of the icing field experiment at Nanyue station, and the results of identification are examined by the camera images. Furthermore, based on deep learning, the environmental layout requirements of ice accretion image identification are discussed. The main conclusions are as follows. When identifying icing weather phenomena at Nanyue station, the probability of correction (PC) is 99.21%, the false acceptance rate (FAR) is 0.28%, and the probability of omission (PO) is 0.51%. The probability of icing identification increases significantly in the initial stage of ice accretion, while that in the sustained stage is stably around 99.0%, and in the dissipation stage it gradually decreases. False acceptance and omission occur occasionally during the initiation and dissipation stages, the transition period between daytime and night, and the nighttime when the pictures are not clear enough. The test results show that the artificial intelligence identification model established in this paper can extract the key features of icing in different stages of an icing lifetime, and the identification result is good. In addition, the false acceptance and omission can be further eliminated by using the meteorological conditions criteria and judging the consistency of identification. This method can provide important technical support for the automatic observation of icing weather phenomena.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
China Meterological Administration. The criterion of surface meteorological observation, pp. 21–27. China Meteorological Press, Beijing (2003)
Huang, X.Y., Li, Z.X., Li, C., et al.: Analysis on extreme freeze catastrophic weather of Hunan in 2008. Meteorol. Mon. 34(11), 47–53 (2008)
Hu, W.D., Yang, K., Huang, X.Y., et al.: Analysis on a severe convection triggered by gust front in Yinchuan with radar data. Plateau Meteorol. 34(5), 1452–1464 (2015)
Wang, Z.Y., Ding, Y.H., He, J.H., et al.: An updating analysis of the climate change in China in recent 50 years. ACTA Meteorol. Sin. 62(2), 228–236 (2004)
Liang, S.J., Ding, Y.H., Zhao, N., et al.: Analysis of the interdecadal changes of the wintertime surface air temperature over mainland China and regional atmospheric circulation characteristics during 1960–2013. Chin. J. Atmos. Sci. 38(5), 974–992 (2014)
Xing, H.Y., Zhang, J.Y., Xu, W., et al.: Development and prospect of automatic meteorological observation technology on the ground. J. Electron. Measur. Instrum. 31(10), 1534–1542 (2017)
Ma, S.J., Wu, K.J., Chen, D.D., et al.: Automated present weather observing system and experiment. Meteorol. Mon. 37(9), 1166–1172 (2011)
Liu, L.Y., Lan, M.C., Zhu, X.W., et al.: The Comparative analysis of two cloud products of FY2G satellite in Hunan Province. Torrential Rain Disasters 36(2), 164–170 (2017)
Hinton, G.E., Osindero, S., TeH, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527 (2006)
Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(28), 504–507 (2006)
Yu, B.,Li, S., Xu, S.X., et al.: Deep learning: the key to open big data era. J. Eng. Stud. no. 3, 233–243 (2014)
Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)
Chan, C.H., Pang, G.K.H.: Fabric defect detection by Fourier analysis. IEEE Trans. Ind. Appl. 36(5), 1267–1276 (2000)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Image net classification with deep convolutional neural networks. In: Proceedings of International Conference on Neural Information Processing System, pp. 1097–1105 (2012)
Wang, Z.Y., Zhang, Q., Chen, Y., et al.: Characters of meteorological disasters caused by the extreme synoptic process in early 2008 over China. Clim. Chang. Res. 4(2), 63–67 (2008)
Gang, H., Chen, L.J., Jia, X.L., et al.: Analysis of the severe cold surge, ice-snow and frozen disasters in South China during january 2008: II possible climatic causes. Meteorol. Mon. 34(4), 101–106 (2008)
Ye, C.Z., Wu, X.Y., Huang, X.Y.: A synoptic analysis of the unprecedented severe event of the consecutive cryogenic freezing rain in Hunan Province. Acta Meteorologica Sin. 67(3), 488–500 (2009)
Acknowledgement
This research was funded by the Small Business Construction Project of China Meteorological Administration (2018) “Comprehensive Meteorological Observation Intelligent Analysis and Identification System Construction” (QXPG20174022) and Special Project for Capacity Building of Meteorological Forecasting of Hunan Meteorological Bureau (2016-2017) “Meteorological element product improvement based on multi-source data fusion (YBNL16-04)”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Huang, X., Ye, C., Cai, R., Zhang, Y., Liu, L., Fu, C. (2019). Application of Artificial Intelligence on the Image Identification of Icing Weather Phenomena. In: Sun, S., Fu, M., Xu, L. (eds) Signal and Information Processing, Networking and Computers. ICSINC 2018. Lecture Notes in Electrical Engineering, vol 550. Springer, Singapore. https://doi.org/10.1007/978-981-13-7123-3_65
Download citation
DOI: https://doi.org/10.1007/978-981-13-7123-3_65
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-7122-6
Online ISBN: 978-981-13-7123-3
eBook Packages: EngineeringEngineering (R0)