Survey on the Application of Deep Learning in Extreme Weather Prediction

Author:

Fang Wei,Xue Qiongying,Shen Liang,Sheng Victor S.

Abstract

Because of the uncertainty of weather and the complexity of atmospheric movement, extreme weather has always been an important and difficult meteorological problem. Extreme weather events can be called high-impact weather, the ‘extreme’ here means that the probability of occurrence is very small. Deep learning can automatically learn and train from a large number of sample data to obtain excellent feature expression, which effectively improves the performance of various machine learning tasks and is widely used in computer vision, natural language processing, and other fields. Based on the introduction of deep learning, this article makes a preliminary summary of the existing extreme weather prediction methods. These include the ability to use recurrent neural networks to predict weather phenomena and convolutional neural networks to predict the weather. They can automatically extract image features of extreme weather phenomena and predict the possibility of extreme weather somewhere by using a deep learning framework.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Atmospheric Science,Environmental Science (miscellaneous)

Reference52 articles.

1. A Fast Learning Algorithm for Deep Belief Nets

2. Learning Deep Architectures for AI

3. Big data air pollution forecast based on deep learning;Yin;China Environ. Manag.,2015

4. Opportunities and challenges of artificial intelligence in meteorological sciences;He;China Inf. Technol.,2019

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