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Title: VARENN: graphical representation of periodic data and application to climate studies
Authors: Ise, Takeshi  kyouindb  KAKEN_id
Oba, Yurika  kyouindb  KAKEN_id
Author's alias: 伊勢, 武史
大庭, ゆりか
Keywords: Climate and Earth system modelling
Statistics
Issue Date: 6-Jul-2020
Publisher: Springer Nature
Journal title: npj Climate and Atmospheric Science
Volume: 3
Thesis number: 26
Abstract: 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 two-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 the 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. We successfully illustrated the importance of short-term (monthly) fluctuations in the model accuracy, suggesting that our AI-based approach grasped some previously unknown patterns that are indicators of succeeding climate trends. VARENN is thus an effective method to summarize spatiotemporal data objectively and accurately.
Description: 機械学習による世界の気候パターンの分類に成功 --30年間の気候データを画像化して深層学習で識別--. 京都大学プレスリリース. 2020-07-28.
Rights: © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
URI: http://hdl.handle.net/2433/252837
DOI(Published Version): 10.1038/s41612-020-0129-x
Related Link: https://www.kyoto-u.ac.jp/ja/research-news/2020-07-28
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