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Identification of Weather Phenomena Based on Lightweight Convolutional Neural Networks

Congcong Wang1, 2, 3, Pengyu Liu1, 2, 3, *, Kebin Jia1, 2, 3, Xiaowei Jia4, Yaoyao Li1, 2, 3

1 Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.
2 Beijing Laboratory of Advanced Information Networks, Beijing, 100124, China.
3 Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China.
4 Department of Computer Science and Engineering, University of Minnesota, Twin Cities, USA.

* Corresponding Author: Pengyu Liu. Email: email.

Computers, Materials & Continua 2020, 64(3), 2043-2055.


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 problems. However, lightweight models often result in significant performance losses. To this end, after taking a deep dive into a large number of lightweight models and summarizing their shortcomings, we propose a novel lightweight CNNs model which is constructed based on new building blocks. The experimental results show that the model proposed in this paper has comparable performance with the mainstream non-lightweight model while also saving 25 times of memory consumption. Such memory reduction is even better than that of existing lightweight models.


Cite This Article

C. Wang, P. Liu, K. Jia, X. Jia and Y. Li, "Identification of weather phenomena based on lightweight convolutional neural networks," Computers, Materials & Continua, vol. 64, no.3, pp. 2043–2055, 2020.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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