Abstract
In recent years, SVM (Support Vector Machine) has been widely used in the field of weather forecasting, especially in the medium and long-term weather forecasting, but it is seldom used in the precipitation nowcasting. Without considering other meteorological factors, this paper uses SVM method in precipitation nowcasting based on the radar images. The statistical results of four difference thunderstorm events shown that the method based on SVM has good performance in the precipitation nowcastings in 0-2 h lead-time forecasting.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yu, C., Yongyi, X.: A new method for dealing with non-linear classification and regression problems (1) - brief introduction of support vector machine method. J. Appl. Meteorol. 15(3), 345–354 (2004)
Feng, H., Chen, Y.: Application of Support Vector Machine (SVM) in weather forecasting, a new method for dealing with non-linear classification and regression problems. J. Appl. Meteorol. 15(3), 335–365 (2004)
Li, Z., Ma, W., et al.: Application of support vector machine in short-term climate prediction. Meteorology 32(5), 58–60 (2016)
Xiong, Q., Zeng, X.: Application and improvement of SVM method in precipitation forecast. Meteorology 34(12), 90–95 (2008)
He, J., Chen, J., et al.: A multi-time scale SVM method for local short-term rainfall prediction. Meteorology 43(4), 402–412 (2017)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Li, H.: Statistical Learning Method, pp. 95–130. Tsinghua University Press (2012)
Zhou, Z.: Machine Learning. Tsinghua University Press, Beijing (2016)
Tobler, W.R.: A computer movie simulating urban growth in the Detroit region. Econ. Geogr. 46(Suppl. 1), 234–240 (1970)
Li, L., He, Z., Chen, S., Mai, X.-F., Hu, B., Li, S.: Subpixel-based precipitation nowcasting with the pyramid Lucas-Kanade optical flow technique. Atmosphere 9(7), 260 (2018)
Acknowledgement
This research was financially supported by the Natural Science Foundation of Guangxi (NO. 2018JJA150144,2018GXNSFAA294079) and the National Science Foundation of China (NO. 61562008, 41401524, 4166010274).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Mai, X., Zhong, H., Li, L. (2020). Using SVM to Provide Precipitation Nowcasting Based on Radar Data. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_37
Download citation
DOI: https://doi.org/10.1007/978-3-030-32591-6_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32590-9
Online ISBN: 978-3-030-32591-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)