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首页> 外文期刊>Expert Systems with Application >SST clustering for winter precipitation prediction in southeast of Iran: Comparison between modified K-means and genetic algorithm-based clustering methods
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SST clustering for winter precipitation prediction in southeast of Iran: Comparison between modified K-means and genetic algorithm-based clustering methods

机译：SST聚类用于伊朗东南部冬季降水预测：改进的K均值与基于遗传算法的聚类方法的比较

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摘要

In this paper, two innovative approaches for temporal clustering of sea surface temperature (SST) data using genetic algorithm (GA) and K-means clustering methods are introduced. In both methods, new approaches have been developed to consider the relationship between variations of the variable being clustered (SST in this study) and another climatic variable (precipitation in this study) in selection of clusters. In the case study, these models are used for clustering SST in selected geographical zones in Gulf of Oman, Arabian Sea, and the northern part of Indian Ocean with respect to the precipitation variations in the selected rain gauges in Sistan and Baluchestan Province in southeast of Iran. For this purpose, fitness function of the GA model and Euclidean distance in the modified K-means method have been formulated to minimize the variance of the precipitation data associated with each selected cluster. Application of these modified clustering methods in the case study has resulted in temporal classification of SST data which also represent below and above normal precipitation seasons in the selected rain gauges. The results of the two clustering techniques have been used for development of seasonal precipitation prediction guidelines based on the SST variations in the selected geographical zones. The results have also shown that these models can be effectively used for prediction of below and above normal precipitation seasons in the study area. Comparison between the results of this study with the official forecasts of the Islamic Republic of Iran Meteorological Organization (IRIMET) has shown significant improvements.
机译：本文介绍了使用遗传算法（GA）和K-means聚类方法对海面温度（SST）数据进行时间聚类的两种创新方法。在这两种方法中，都已经开发出新的方法来考虑正在聚类的变量（本研究中的SST）与选择聚类时另一个气候变量（本研究中的降水）之间的关系。在案例研究中，这些模型用于将阿曼湾，阿拉伯海和印度洋北部某些地理区域中的海表温度聚类，以与该国东南部锡斯坦和Bal路支斯坦省某些雨量计中的降水变化有关。伊朗。为此，在改进的K均值方法中制定了GA模型的适应度函数和欧几里得距离，以最小化与每个所选聚类相关的降水数据的方差。这些改进的聚类方法在案例研究中的应用导致了SST数据的时间分类，该数据也代表了所选雨量表中正常降水季节以下和以上的情况。两种聚类技术的结果已用于根据所选地理区域中SST的变化制定季节性降水预测准则。结果还表明，这些模型可以有效地用于预测研究区域正常降水季节以下和以上。这项研究的结果与伊朗伊斯兰共和国气象组织（IRIMET）的官方预报之间的比较显示出明显的改进。

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