Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets

Brief Project Information

The study presents the "first application of Deep Learning techniques as an alternative methodology for climate extreme events detection." The developed deep Convolutional Neural Network (CNN) classification system is able to learn high-level representations of a broad class of patterns from labeled data and achieves 89%-99% of accuracy in detecting extreme events.


United States                
Published in 2016
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Sustainability Review

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Editor's comment for this project's sustainability

Applying deep learning to identify and quantify extreme climate events to help us cope with the impact of climate change has proved to be a very promising approach.

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