Open Access
Issue
ITM Web Conf.
Volume 47, 2022
2022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
Article Number 01017
Number of page(s) 8
Section Computer Science and System Design, Application
DOI https://doi.org/10.1051/itmconf/20224701017
Published online 23 June 2022
  1. LIANG L, PANG W J, LEI Y, WANG Z C, LIANG C. Temporal and spatial distribution characteristics of ground lightning activity in Beijing[J]. Journal of the Meteorological Sciences,2019,39(04):515-523. [Google Scholar]
  2. GAO P, TIAN H, LI J, TAO H T, WANG Z, JIANG Z B. Thunderstorm mining and research based on improved DBScan algorithm[J]. High Voltage Apparatus,2019,55(04):169-177. [Google Scholar]
  3. Raloff J. Extremely bad weather: Studies start linking climate change to current events [J]. Science News, 2012, 182(10): 22-26. [CrossRef] [Google Scholar]
  4. Gillis J. Study Indicates a Greater Threat of Extreme Weather [J]. The New York Times, 2012, 26. [Google Scholar]
  5. Mikhailovskii Y P, Sin’kevich A A, Pawar S D, et al. Investigations of the development of thunderstorm with hail. Part 2. Analysis of methods for the forecast and diagnosis of the electrical properties of clouds[J]. Russian Meteorology and Hydrology, 2017, 42(6): 377-387. [CrossRef] [Google Scholar]
  6. JI F. Research on text clustering and thunderstorm prediction model based on Hadoop[D]. Nanjing University of Information Science & Technology,2014. [Google Scholar]
  7. XIE Z M, XU X W, HUANG R F, LIN Q H, LI Z, CHEN X, ZHAO J H. Study on Thunderstorm Weather Prediction Based on HY-FMV model[J]. E-science Technology & Application,2018,9(02):71-78. [Google Scholar]
  8. NI Z, WEN T. A weather prediction model based on CNN and RNN depth neural network – Taking the 6-hour approaching forecast of Thunderstorm in Beijing as an example[J]. Journal on Numerical Methods and Computer Applications, 2018,39(04):299-309. [Google Scholar]
  9. CHEN Y W, ZHENG T, WANG H F, WANG Q, LIANG Y D. Lightning potential prediction based on BP neural network model[J]. Journal of Arid Meteorology,2013,31(03):595-601. [Google Scholar]
  10. Wang B, Gu X, Ma L, et al. Temperature error correction based on BP neural network in meteorological wireless sensor network[J]. International Journal of Sensor Networks, 2017, 23(4): 265-278. [CrossRef] [Google Scholar]
  11. ZHANG X, GUO X, YU Y H, CHEN J J, WANG J H, PENG B Z. A power network fault diagnosis method based on Genetic Algorithm Optimized BP neural network[J]. Technology Innovation and Application, 2019(29):15-17. [Google Scholar]
  12. SHI Z Q, LIANG X L, ZHANG J Q, LIU L, ZHANG X Q. Attack area solution method based on GWO-BP neural network[J]. Flight Dynamics, 2019, 37(03):64-67+92. [Google Scholar]
  13. Xu L, Wang H, Lin W, et al. GWO-BP neural network based OP performance prediction for mobile multiuser communication networks[J]. IEEE Access, 2019, 7: 152690-152700. [CrossRef] [Google Scholar]
  14. Tian Y, Yu J, Zhao A. Predictive model of energy consumption for office building by using improved GWO-BP[J]. Energy Reports, 2020, 6: 620-627. [CrossRef] [Google Scholar]
  15. HU C J, ZHANG J. An immune differential evolution algorithm using clonal selection [J]. Computer application research[J]. Application Research of Computers, 2013, 30(06): 1640-1642+1651. [Google Scholar]
  16. Ukkonen P, Manzato A, Mäkelä A. Evaluation of thunderstorm predictors for Finland using reanalyses and neural networks[J]. Journal of Applied Meteorology and Climatology, 2017, 56(8): 2335-2352. [Google Scholar]

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