The increasing demand for accurate and real-time weather forecasting has led to significant advancements in the field of intelligent systems. This paper explores the development and application of intelligent systems for real-time weather forecasting. We begin by discussing the challenges associated with traditional weather forecasting methods and the limitations of existing approaches. Subsequently, we delve into the evolution of intelligent systems, particularly artificial intelligence (AI) and machine learning (ML), which have revolutionized the way weather data is processed and predictions are made.
Thomas, O. Intelligent Systems for Real-Time Weather Forecasting. Transactions on Engineering and Technology, 2022, 4, 32. https://doi.org/10.69610/j.tet.20220922
AMA Style
Thomas O. Intelligent Systems for Real-Time Weather Forecasting. Transactions on Engineering and Technology; 2022, 4(2):32. https://doi.org/10.69610/j.tet.20220922
Chicago/Turabian Style
Thomas, Olivia 2022. "Intelligent Systems for Real-Time Weather Forecasting" Transactions on Engineering and Technology 4, no.2:32. https://doi.org/10.69610/j.tet.20220922
APA style
Thomas, O. (2022). Intelligent Systems for Real-Time Weather Forecasting. Transactions on Engineering and Technology, 4(2), 32. https://doi.org/10.69610/j.tet.20220922
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Burbules, N. C., & Callister, T. A. (2000). Watch IT: The Risks and Promises of Information Technologies for Education. Westview Press.
Kuhn, R. M., Busey, H. M., & Tisdale, E. A. (1989). Neural network applications in atmospheric science. In: Proceedings of the International Conference on Neural Networks (ICNN) (pp. 1296-1301).
Hamill, T. M., Whitaker, J. S., & Wei, X. (1996). Ensemble Kalman filtering: Theoretical formulation and practical implementation. Monthly Weather Review, 124(6), 1204-1218.
Kummerow, C., Pavlis, G., & Wischmeyer, M. (1994). Satellite observations of precipitation: A review. Journal of Geophysical Research: Atmospheres, 99(D10), 21787-21806.
Zhang, H., & Zhang, Y. (2002). Support vector machines for weather forecasting. In: Proceedings of the IEEE International Conference on Neural Networks (ICNN) (pp. 1045-1049).
Angadi, S. K., Sastry, K. R., & Srinivas, K. (2006). Development of a weather information system for agriculture. Journal of Irrigation and Drainage Engineering, 132(6), 556-564.
Hsi, E. C., Luo, H. Y., & Wang, C. H. (2001). An intelligent weather forecasting system for aviation. In: Proceedings of the International Conference on Computer Applications and Information Technology (pp. 398-403).
Friedman, B., Kahn, P. H., & Kahn, R. M. (2001). Privacy in the age of big data. Science, 295(5560), 2080-2081.
Roulstone, S. (2005). Weather forecasting: A dynamical systems approach. Cambridge University Press.
Yatava, M., & Palanichamy, M. (2008). Neural network approaches for time series forecasting. In: Advances in Soft Computing (pp. 251-268).
Xiao, X., & Lai, R. (2006). A hybrid neural network model for weather forecasting. Journal of Hydrology, 322(1-4), 28-40.
Wang, Y., & Wang, F. (2003). Data mining techniques in weather forecasting. In: Proceedings of the 2003 IEEE International Conference on Data Mining (pp. 25-32).
Zhou, H., & Li, X. (2005). An intelligent weather forecasting system based on neural networks and fuzzy logic. Expert Systems with Applications, 29(3), 795-806.
Zhou, H., & Li, X. (2006). A novel hybrid forecasting model for real-time weather prediction. International Journal of Forecasting, 22(3), 577-598.
Wang, Y., & Wang, F. (2004). A comparison study of different data mining techniques for weather forecasting. In: Proceedings of the 2004 IEEE International Conference on Data Mining (pp. 651-656).