Congcong Wang1, 2, 3, Pengyu Liu1, 2, 3, *, Kebin Jia1, 2, 3, Xiaowei Jia4, Yaoyao Li1, 2, 3
CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 2043-2055, 2020, DOI:10.32604/cmc.2020.010505
- 30 June 2020
Abstract Weather phenomenon recognition plays an important role in the field of
meteorology. Nowadays, weather radars and weathers sensor have been widely used for
weather recognition. However, given the high cost in deploying and maintaining the
devices, it is difficult to apply them to intensive weather phenomenon recognition.
Moreover, advanced machine learning models such as Convolutional Neural Networks
(CNNs) have shown a lot of promise in meteorology, but these models also require
intensive computation and large memory, which make it difficult to use them in reality.
In practice, lightweight models are often used to solve such More >
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