Rainfall Model Using Principal Component Regression Analysis with R Software in Sulawesi

Annisa Alma Yunia, D. A. Kusuma, B. Suhandi, B. N. Ruchjana
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Abstract

Indonesia is a tropical country that has two seasons, rainy and dry. Nowadays, the earth is experiencing the climate change phenomenon which causes erratic rainfall. The rainfall is influenced by several factors, one of which is the local scale factor. This research was aim ed to build a rainfall model in Sulawesi to find out how the rainfall relationship with local scale factor in Sulawesi. In this research, the data used were secondary data which consisted of 15 samples with 6 variables from Badan Pusat Statistik (BPS). The limitation of the sample size in this study was due to the limited secondary data available in the field . The data was processed using Principal Component Regression Analysis . The first step was reducing local scale factor variables so that the principal component variable could be obtained that can explain variability from the original data which then that variable was analyze d using principal regression analysis. The data were analyzed by utilizing R Studio software. The results show that two principal component variables can explain 75.2% of the variability of original data and only one principal component variable that was significant to the rainfall variable. The regression model explain ed that the relationship between rainfall , humidity, air temperature, air pressure , and solar radiation was in the same direction while the relationship between rainfall and wind velocity was not in the same direction. Overall , the results of the study provide d an overview of the application of the Principal Component Regression analysis to model the rainfall phenomenon in the Sulawesi region using the R program.
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基于R软件的苏拉威西岛降雨主成分回归模型
印度尼西亚是一个热带国家,有两个季节,多雨和干燥。如今,地球正在经历气候变化现象,导致降雨不稳定。降雨受多个因素的影响,其中一个因素是局地尺度因素。本研究旨在建立苏拉威西岛的降雨模型,以了解苏拉威西岛的降雨与当地尺度因子的关系。本研究使用的数据为来自巴丹市统计局(BPS)的15个样本、6个变量的二次数据。本研究样本量的限制是由于实地可用的次要数据有限。采用主成分回归分析对数据进行处理。第一步是减少局部尺度因子变量,这样就可以得到主成分变量,它可以解释原始数据的可变性,然后使用主回归分析对该变量进行分析。利用R Studio软件对数据进行分析。结果表明,两个主成分变量可以解释75.2%的原始数据变异,只有一个主成分变量对降雨变量有显著性。回归模型解释了降水与湿度、气温、气压、太阳辐射的关系为同一方向,而降水与风速的关系为不同方向。总体而言,研究结果概述了主成分回归分析在苏拉威西地区使用R程序模拟降雨现象的应用。
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