Ngoan Thanh Trieu, Hiep Xuan Huynh, Vincent Rodin and Bernard Pottier.
Interpretable Machine Learning for Meteorological Data.
CMLSC 2021, 5th International Conference on Machine Learning and Soft Computing, page 11-17, Sanya (China), 29-31 January, 2021.
Abstract:
Weather forecasting is the task to predict the state of the atmosphere in a given location. In the past, the weather forecast has been done through physical models of the atmosphere as a fluid. It becomes the problem of solving sophisticated equations of fluid dynamics. In recent years, machine learning algorithms have been used to speed up weather data modeling, a computationally intensive task. Machine learning algorithms learn from data and produce relevant predictions. In addition to prediction, there is a need of providing knowledge about domain relationships inside the data. This paper provides a new approach using interpretable machine learning for explaining the characteristic variables of meteorological data. Interpretable machine learning is the use of machine learning models for the extraction of knowledge in the data. An illustration is shown on characteristic variables of meteorological data.
Keywords: Environment Simulations, Weather Data, BUFR/Express, Interpretable Machine Learning.
[doi:10.1145/3453800.3453803] [Trieu21a.pdf]

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