Graph-based local climate classification in Iran,International Journal of Climatology

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Graph-based local climate classification in Iran
International Journal of Climatology ( IF 3.5 ) Pub Date : 2021-07-21 , DOI: 10.1002/joc.7306
Neda Akrami ₁ , Koorush Ziarati ₁ , Soumyabrata Dev ₂

Affiliation

In this paper, we introduce a novel graph-based method to classify the regions with similar climate in a local area. We refer our proposed method as graph partition based method (GPBM). Our proposed method attempts to overcome the shortcomings of the current state-of-the-art methods in the literature. It has no limit on the number of variables that can be used and also preserves the nature of climate data. To illustrate the capability of our proposed algorithm, we benchmark its performance with other state-of-the-art climate classification techniques. The climate data are collected from 24 synoptic stations in Fars province in southern Iran. The data include seven climate variables stored as time series from 1951 to 2017. Our results exhibit that our proposed method performs a more realistic climate classification with less computational time. It can save more information during the climate classification process and is therefore efficient in further data analysis. Furthermore, using our method, we can introduce seasonal graphs to better investigate seasonal climate changes. To the best of our knowledge, our proposed method is the first graph-based climate classification system.

中文翻译：

伊朗基于图表的局部气候分类

在本文中，我们介绍了一种新的基于图的方法来对当地气候相似的区域进行分类。我们将我们提出的方法称为基于图分区的方法（GPBM）。我们提出的方法试图克服文献中当前最先进方法的缺点。它对可以使用的变量数量没有限制，并且还保留了气候数据的性质。为了说明我们提出的算法的能力，我们用其他最先进的气候分类技术对其性能进行了基准测试。气候数据来自伊朗南部法尔斯省的 24 个气象站。这些数据包括从 1951 年到 2017 年作为时间序列存储的七个气候变量。我们的结果表明，我们提出的方法可以用更少的计算时间执行更现实的气候分类。它可以在气候分类过程中保存更多的信息，因此在进一步的数据分析中是有效的。此外，使用我们的方法，我们可以引入季节性图表来更好地研究季节性气候变化。据我们所知，我们提出的方法是第一个基于图的气候分类系统。

更新日期：2021-07-21

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