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Correlation of Climate Variability and Burned Area in Borneo using Clustering Methods

机译：使用聚类方法与婆罗洲气候变异性和烧伤区域的相关性

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The island of Borneo has faced seasonal forest fires for decades. This phenomenon is worsening during dry seasons, especially when droughts are concurrent with the El Ni?o-Southern Oscillation (ENSO) phenomenon. Climate is therefore one of the drivers of the fire phenomenon. This paper studies the relationship between climate variables, namely temperature, precipitation, relative humidity, and wind speed, and the occurrence of forest fire using two clustering methods, K-means and Fuzzy C-means (FCM) clustering methods. Borneo is clustered into four areas based on burned area data obtained from Global Fire Emission Data (GFED). It is also clustered according to the combinations of climate variables. Both methods reach the highest correlation between the climate variable and the burned area clusters in September. The K-means method gives a correlation of -0.54 while the FCM gives -0.55. In August until October, relative humidity provides the dominant correlation affecting burned area, even though an additional precipitation or wind variable slightly increases the correlation in the FCM method. In November, temperature largely contributed to the burned area by a positive correlation of 0.31 in K-means and 0.33 in FCM. The evaluation performance of the methods is conducted by an internal validation called the Silhouette index. Both methods have positive index values ranging from 0.39 to 0.69 and the maximum value is influenced by the wind cluster. This indicates that the clustering methods applied in this paper can identify one or a combination of climate variables into dense and well-separated clusters.
机译：几十年来，婆罗洲岛面临着季节性森林火灾。这种现象在干燥季节期间恶化，特别是当干旱与EL Ni？O-Southern振荡（ENSO）现象同时发生时。因此，气候是火灾现象的驱动因素之一。本文研究了气候变量，即温度，降水，相对湿度和风速之间的关系，以及使用两种聚类方法的森林火灾发生，K均值和模糊C型（FCM）聚类方法。婆罗洲基于从全球火灾发射数据（GFED）获得的烧毁区域数据集聚集成四个区域。根据气候变量的组合也是聚集的。两种方法均在9月份达到气候变量与烧毁区域集群之间的最高相关性。 K-means方法提供-0.54的相关性，而FCM给出-0.55。 8月至10月，相对湿度提供影响烧毁区域的主导相关性，即使额外的降水或风变量略微增加了FCM方法中的相关性。 11月，温度大部分地通过0.31以k-means的正相关和Fcm呈0.33的烧结区域。该方法的评估性能由称为轮廓索引的内部验证进行。两种方法的正指数值范围为0.39至0.69，最大值受风簇的影响。这表明本文应用的聚类方法可以将气候变量的一个或组合识别成密集和良好分离的簇。

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