VARENN: Graphical representation of spatiotemporal data and application to climate studies
VARENN: Graphical representation of spatiotemporal data and application to climate studies
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发表时间：
2019-07
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通讯作者：
T. Ise;Y. Oba
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作者：
T. Ise;Y. Oba
分析和利用时空大数据是气候变化研究的重要内容。然而，由于统计框架的限制，这些数据没有完全纳入气候模式。本文采用VARENN（神经网络环境的视觉增强表示）将1901-2016年的月度气候观测数据有效地总结为二维图形图像。使用红色、绿色和蓝色通道的彩色图像，三个不同的变量同时表示在一个单一的图像。对于全局数据集，模型通过卷积神经网络进行训练。这些模型成功地对温度和降水的上升和下降进行了分类。此外，观察到输入变量和目标变量之间的相似性对模型精度有显着影响。输入变量具有季节和年际变化，其重要性被量化为模型有效性。因此，VARENN是客观、准确地总结时空数据的有效方法。
Analyzing and utilizing spatiotemporal big data are essential for studies concerning climate change. However, such data are not fully integrated into climate models owing to limitations in statistical frameworks. Herein, we employ VARENN (visually augmented representation of environment for neural networks) to efficiently summarize monthly observations of climate data for 1901-2016 into 2-dimensional graphical images. Using red, green, and blue channels of color images, three different variables are simultaneously represented in a single image. For global datasets, models were trained via convolutional neural networks. These models successfully classified rises and falls in temperature and precipitation. Moreover, similarities between the input and target variables were observed to have a significant effect on model accuracy. The input variables had both seasonal and interannual variations, whose importance was quantified for model efficacy. VARENN is thus an effective method to summarize spatiotemporal data objectively and accurately.