Tornado Storm Data Synthesization Using Deep Convolutional Generative Adversarial Network
Tornado Storm Data Synthesization Using Deep Convolutional Generative Adversarial Network
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DOI：
10.13016/m2gglo-btj5
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
C. Barajas;M. Gobbert;Jianwu Wang
中科院分区：
文献类型：
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作者：
C. Barajas;M. Gobbert;Jianwu Wang
。由于天气模拟的复杂性，用当前的模型预测猛烈的风暴和危险的天气状况可能需要很长时间。机器学习有可能更快地对龙卷风天气模式进行分类，从而向公众发出更及时的警报。将机器学习应用于龙卷风预测的一个挑战是龙卷风数据与非龙卷风数据之间的不平衡。为了获得更平衡的数据，我们在这项工作中创建了一个新的数据综合系统，通过实施深度卷积生成对抗网络（DCGAN）来增强龙卷风风暴数据，并将其输出与自然数据进行定性比较。
. Predicting violent storms and dangerous weather conditions with current models can take a long time due to the immense complexity associated with weather simulation. Machine learning has the potential to classify tornadic weather patterns much more rapidly, thus allowing for more timely alerts to the public. A challenge in applying machine learning in tornado prediction is the imbalance between tornadic data and non-tornadic data. To have more balanced data, we created in this work a new data synthesization system to augment tornado storm data by implementing a deep convolutional generative adversarial network (DCGAN) and qualitatively compare its output to natural data.