@Article{cmc.2020.010505,
AUTHOR = {Congcong Wang, Pengyu Liu, Kebin Jia, Xiaowei Jia, Yaoyao Li},
TITLE = {Identification of Weather Phenomena Based on Lightweight  Convolutional Neural Networks},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {3},
PAGES = {2043--2055},
URL = {http://www.techscience.com/cmc/v64n3/39475},
ISSN = {1546-2226},
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 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.},
DOI = {10.32604/cmc.2020.010505}
}