Insolation prediction using air pollutants and meteorological variables<sup>†</sup>
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전체 글
(2) 998. Yeongeun Hwang · Dayoung Kang · Myunghwan Na · Sanghoo Yoon. ᄋᄋ ᅵ ᆼᄒ ᅭ ᆫᄋ ᅡ ᆫᄀ ᅧ ᅮᄅ ᅩ Lee ᄃ ᆼ (2017)ᄋ ᅳ ᆫᄀ ᅳ ᅵᄉ ᆼᄌ ᅡ ᅡᄅ ᅭᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄀ ᅧ ᅩ, Jung ᄃ ᆼ (2011)ᄀ ᅳ ᅪ Won ᄃ ᆼ (2011)ᄋ ᅳ ᆫᄋ ᅳ ᆫᄅ ᅮ ᆼᄋ ᅣ ᅳ ᅩᄋ ᄅ ᆯᅡ ᅵ ᄉᄅ ᆼᄋ ᅣ ᆯᄎ ᅳ ᅮᄌ ᆼᄒ ᅥ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄋ ᅵᅬ ᄋᄋ ᅦᄃ ᅩᄀ ᆼᄉ ᅧ ᅡᄃ ᅩᅪ ᄋᄀ ᆼᄉ ᅧ ᅡᄇ ᆼᄒ ᅡ ᆼᄋ ᅣ ᆯᄀ ᅳ ᅩᄅ ᅧᄒ ᆫᄋ ᅡ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᅨᄎ ᆨᄆ ᅳ ᅩᄒ ᆼ (Yun, 2009)ᄀ ᅧ ᅪᄉ ᆸᄃ ᅳ ᅩ ᅪᄆ ᄋ ᅵᄉ ᅦᄆ ᆫᄌ ᅥ ᅵᄀ ᅡᄋ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᅦᄆ ᅵᄎ ᅵᄂ ᆫᄋ ᅳ ᆼᄒ ᅧ ᆼ (Lee ᄃ ᅣ ᆼ, 2017) ᄃ ᅳ ᆼᄋ ᅳ ᅴᄋ ᆫᄀ ᅧ ᅮᄀ ᅡᄉ ᅮᄒ ᆼᄃ ᅢ ᅬᄋ ᆻᄃ ᅥ ᅡ. ᅬᄀ ᄎ ᆫᄀ ᅳ ᆫᄎ ᅪ ᆨᄌ ᅳ ᆼᄇ ᅡ ᅵᄆ ᆾᄐ ᅵ ᆼᄉ ᅩ ᆫᄉ ᅵ ᅵᄉ ᅳᄐ ᆷᄇ ᅦ ᆯᄃ ᅡ ᆯᄅ ᅡ ᅩᄋ ᆫᄒ ᅵ ᅡᄋ ᅧᄀ ᅵᄉ ᆼᄌ ᅡ ᆫᄉ ᅥ ᆫᄌ ᅡ ᅡᄋ ᆫᄋ ᅯ ᅴᄀ ᅩᄃ ᅩᄒ ᅪᄀ ᅡᄋ ᅵᄅ ᅮᄋ ᅥᄌ ᆻᄃ ᅧ ᅡ. ᄋ ᅵᄋ ᅦᄃ ᅢᄋ ᆼᄅ ᅭ ᆼ ᅣ ᅵᄉ ᄀ ᆼᄀ ᅡ ᆫᄎ ᅪ ᆨᄌ ᅳ ᅡᄅ ᅭᄅ ᆯᄀ ᅳ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᅳ ᅳ ᆯ ᄋᄐ ᆼᄒ ᅩ ᅢᄀ ᅵᄉ ᆼᄋ ᅡ ᅨᄎ ᆨᄋ ᅳ ᅦᄀ ᆫᄒ ᅪ ᆫᄋ ᅡ ᆫᄀ ᅧ ᅮᄀ ᅡᄒ ᆯᄇ ᅪ ᆯᄒ ᅡ ᅵᄉ ᅮᄒ ᆼᄃ ᅢ ᅬᄀ ᅩᄋ ᆻᄃ ᅵ ᅡ (Jeong, 2020). ᄐ ᆨᄒ ᅳ ᅵ ᅵᄀ ᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅴᄃ ᅡᄋ ᆼᄒ ᅣ ᆫᄋ ᅡ ᆯᄀ ᅡ ᅩᄅ ᅵᄌ ᆷᄋ ᅳ ᅦᄋ ᅴᄒ ᅢᄃ ᆫᄀ ᅡ ᅵᄀ ᆫᄆ ᅡ ᆾᄌ ᅵ ᆨᄋ ᅡ ᆫᄇ ᅳ ᆫᄒ ᅧ ᅪᄅ ᆯᄋ ᅳ ᅨᄎ ᆨᄒ ᅳ ᅡᄂ ᆫᄃ ᅳ ᅦᄂ ᇁᄋ ᅩ ᆫᄉ ᅳ ᅮᄌ ᆫᄋ ᅮ ᅴᄋ ᅨᄎ ᆨᄅ ᅳ ᆨᄋ ᅧ ᆯᄇ ᅳ ᅩᄋ ᆫᄃ ᅵ ᅡ (Kimᄀ ᅪ Kim, 2017). Jeong (2020)ᄋ ᆫᄉ ᅳ ᆷᄎ ᅵ ᆼᄉ ᅳ ᆫᅧ ᅵ ᆼ ᄀᄆ ᆼ, SVM, ᄅ ᅡ ᆫᅥ ᅢ ᆷ ᄃᄑ ᅩᄅ ᅦᄉ ᅳᄐ ᅳᄃ ᆼᄀ ᅳ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᅳ ᅳ ᆯ ᄋᄒ ᆯᄋ ᅪ ᆼᄒ ᅭ ᅡᄋ ᅧᄃ ᆫᄀ ᅡ ᅵ ᆼᄉ ᅮ ᄑ ᆨᅨ ᅩ ᄋᄎ ᆨᄋ ᅳ ᆯ ᄒ ᅳ ᅡᄋ ᆻᄀ ᅧ ᅩ, Anᄀ ᅪ Lim (2020)ᄋ ᆫ ᄉ ᅳ ᆫᅧ ᅥ ᆼ ᄒᄑ ᆫᄇ ᅡ ᆯᄇ ᅧ ᆫᄉ ᅮ ᆨ, SVM, ᄅ ᅥ ᆫᄃ ᅢ ᆷᄑ ᅥ ᅩᄅ ᅦᄉ ᅳᄐ ᅳ ᄆ ᆾ ᄅ ᅵ ᅩᄌ ᅵᄉ ᅳᄐ ᆨ ᄒ ᅵ ᅬᄀ ᅱᄇ ᆫᄉ ᅮ ᆨᄋ ᅥ ᆯ ᅳ ᆯᄋ ᅪ ᄒ ᆼᅡ ᅭ ᄒᄋ ᅧᄋ ᅵᄉ ᆫᄒ ᅡ ᆼᄆ ᅧ ᅵᄉ ᅦᄆ ᆫᄌ ᅥ ᅵᄅ ᆯᄋ ᅳ ᅨᄎ ᆨᄒ ᅳ ᅡᄋ ᆻᄀ ᅧ ᅩ, Kim ᄃ ᆼ (2020)ᄋ ᅳ ᆫᄋ ᅳ ᅴᄉ ᅡᄀ ᆯᅥ ᅧ ᆼ ᄌᄂ ᅡᄆ ᅮ, ᄇ ᅢᄀ ᆼᄆ ᅵ ᆾᄇ ᅵ ᅮᄉ ᅳᄐ ᆼᄃ ᅵ ᆼᄋ ᅳ ᆯᄌ ᅳ ᆨᄋ ᅥ ᆼᄒ ᅭ ᅡ ᅧᄀ ᄋ ᅡᄆ ᆷᄋ ᅮ ᅨᄎ ᆨᄆ ᅳ ᅩᄒ ᆼᄋ ᅧ ᆯᄇ ᅳ ᅵᄀ ᅭᄆ ᆾᄀ ᅵ ᆷᄌ ᅥ ᆼᄒ ᅳ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄀ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᅳ ᅳ ᆯ ᄋᄒ ᆯᄋ ᅪ ᆼᄒ ᅭ ᆫᄋ ᅡ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᅨᄎ ᆨᄆ ᅳ ᅩᄒ ᆼᄋ ᅧ ᆫᄋ ᅳ ᅴᄉ ᅡᄀ ᆯᅥ ᅧ ᆼ ᄌᄂ ᅡᄆ ᅮ, ᄅ ᆫᄃ ᅢ ᆷᄑ ᅥ ᅩ ᅦᄉ ᄅ ᅳᅳ ᄐ, ᄉ ᅥᄑ ᅩᄐ ᅳᄇ ᆨᄐ ᅦ ᅥᄆ ᅥᄉ ᆫ (support vector machine, SVM), artificial neural network, long short-term ᅵ memory (LSTM) ᄃ ᆼᄋ ᅳ ᅵᄋ ᆻᄃ ᅵ ᅡ (Lee ᄃ ᆼ, 2017; Kim ᄃ ᅳ ᆼ, 2019; Kim ᄃ ᅳ ᆼ, 2021). ᅳ ᇁᄉ ᅡ ᄋ ᅥᄉ ᆯᄑ ᅡ ᅧᄇ ᆫᄉ ᅩ ᆫᄒ ᅥ ᆼᄋ ᅢ ᆫᄀ ᅧ ᅮᄋ ᅦᄋ ᅴᄒ ᅡᄆ ᆫᄋ ᅧ ᆯᄉ ᅵ ᅡᄅ ᆯᄋ ᅳ ᅨᄎ ᆨᄒ ᅳ ᅡᄀ ᅵᄋ ᅱᄒ ᅢᄀ ᅵᄉ ᆼᄋ ᅡ ᅭᄋ ᆫᄀ ᅵ ᅪᄆ ᅵᄉ ᅦᄆ ᆫᄌ ᅥ ᅵᄃ ᆼᄋ ᅳ ᅬᄇ ᅮᄒ ᆫᄀ ᅪ ᆼᄋ ᅧ ᅭᄋ ᆫᄋ ᅵ ᆯᄀ ᅳ ᅩᄅ ᅧ ᅡᄋ ᄒ ᆻᅡ ᅧ ᄃ. ᄀ ᅳᄅ ᅥᄂ ᅡᄀ ᅵᄉ ᆼᄋ ᅡ ᅭᄋ ᆫᄀ ᅵ ᅪᄆ ᅵᄉ ᅦᄆ ᆫᄌ ᅥ ᅵᄅ ᆯᄃ ᅳ ᆼᄉ ᅩ ᅵᄀ ᅩᄅ ᅧᄒ ᆫᄋ ᅡ ᆯᄉ ᅵ ᅡᄋ ᅨᄎ ᆨᄋ ᅳ ᆫᄀ ᅧ ᅮᄂ ᆫᄌ ᅳ ᆫᄒ ᅵ ᆼᄃ ᅢ ᅬᄌ ᅵᄋ ᆭᄋ ᅡ ᆻᄃ ᅡ ᅡ. ᄄ ᅡᄅ ᅡᄉ ᅥᄋ ᅵᄋ ᆫ ᅧ ᅮᄂ ᄀ ᆫᄀ ᅳ ᅵᄉ ᆼᄌ ᅡ ᅡᄅ ᅭ, ᄃ ᅢᄀ ᅵᄋ ᅩᄋ ᆷᄆ ᅧ ᆯᄌ ᅮ ᆯᄌ ᅵ ᅡᄅ ᅭ, ᄀ ᅳᄅ ᅵᄀ ᅩᄋ ᆫᄅ ᅮ ᆼᄌ ᅣ ᅡᄅ ᅭᄅ ᆯᄒ ᅳ ᆯᄋ ᅪ ᆼᄒ ᅭ ᅡᄋ ᅧᄐ ᅳᄅ ᅵᄀ ᅵᄇ ᆫᅡ ᅡ ᆼ 아 ᆼ ᄉᄇ ᆯᄆ ᅳ ᅩᄒ ᆼᄋ ᅧ ᅳᄅ ᅩᄋ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᆯ ᅳ ᅨᄎ ᄋ ᆨᅡ ᅳ ᄒᄀ ᅩᄌ ᅡᄒ ᆫᄃ ᅡ ᅡ. ᆫᄀ ᅧ ᄋ ᅮᄌ ᅡᄅ ᅭᄂ ᆫ 2015ᄂ ᅳ ᆫ 1ᄋ ᅧ ᆯ 1ᄋ ᅯ ᆯᄇ ᅵ ᅮᄐ ᅥ 2019ᄂ ᆫ 12ᄋ ᅧ ᆯ 31ᄋ ᅯ ᆯᄋ ᅵ ᅴᄉ ᅵᄀ ᆫᄃ ᅡ ᆫᄋ ᅡ ᅱᄋ ᆯᄉ ᅵ ᅡᄅ ᆼ, ᄀ ᅣ ᅵᄉ ᆼᄋ ᅡ ᅭᄋ ᆫ, ᄀ ᅵ ᅮᄅ ᆷᄋ ᅳ ᅴᄋ ᆼ, ᄀ ᅣ ᅳᄅ ᅵ ᅩᄃ ᄀ ᅢᅵ ᄀᄋ ᅩᄋ ᆷᄆ ᅧ ᆯᄌ ᅮ ᆯᄂ ᅵ ᆼᄃ ᅩ ᅩᄋ ᅵᄃ ᅡ. ᄌ ᆼᄀ ᅩ ᆫᄀ ᅪ ᅵᄉ ᆼᄀ ᅡ ᆫᄎ ᅪ ᆨᄉ ᅳ ᅩᅪ ᄋᄃ ᅢᄀ ᅵᄋ ᅩᄋ ᆷᄆ ᅧ ᆯᄌ ᅮ ᆯᄀ ᅵ ᆫᄎ ᅪ ᆨᄉ ᅳ ᅩᄋ ᅴᄋ ᅱᄎ ᅵᄀ ᅡᄉ ᆼᄋ ᅡ ᅵᄒ ᅡᄋ ᅧᄋ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᅵᄀ ᆫᄎ ᅪ ᆨ ᅳ ᆫᄌ ᅬ ᄃ ᆼᄀ ᅩ ᆫᄀ ᅪ ᅵᄉ ᆼᄀ ᅡ ᆫᄎ ᅪ ᆨᄉ ᅳ ᅩᄅ ᆯᄀ ᅳ ᅵᄌ ᆫᄋ ᅮ ᅳᄅ ᅩ 9km ᄋ ᅵᄂ ᅢᄋ ᅱᄎ ᅵᄒ ᆫᄃ ᅡ ᅢᄀ ᅵᄋ ᅩᄋ ᆷᄆ ᅧ ᆯᄌ ᅮ ᆯᄀ ᅵ ᆫᄎ ᅪ ᆨᄉ ᅳ ᅩᄅ ᆯᄉ ᅳ ᆫᅥ ᅥ ᆼ ᄌᄒ ᅡᄋ ᅧᄇ ᆫᄉ ᅮ ᆨᄋ ᅥ ᆯᄉ ᅳ ᅮᄒ ᆼᄒ ᅢ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᆯᄉ ᅵ ᄋ ᅡᄅ ᆼᄋ ᅣ ᅨᄎ ᆨᄋ ᅳ ᆯᄋ ᅳ ᅱᅡ ᆫ ᄒᄀ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅳᄅ ᅩᄇ ᅢᄀ ᆼᄀ ᅵ ᅵᄇ ᆫᄋ ᅡ ᆼᄉ ᅡ ᆼᄇ ᅡ ᆯᄆ ᅳ ᅩᄒ ᆼᄋ ᅧ ᆫᄅ ᅵ ᆫᅥ ᅢ ᆷ ᄃᄑ ᅩᄅ ᅦᄉ ᅳᄐ ᅳᄋ ᅪᄇ ᅮᄉ ᅳᄐ ᆼᄀ ᅵ ᅵᄇ ᆫᄋ ᅡ ᆼᅡ ᅡ ᆼ ᄉᄇ ᆯᄆ ᅳ ᅩᄒ ᆼᄋ ᅧ ᆫ ᅵ gradient boost, ᄀ ᅳᄅ ᅵᄀ ᅩ XGboostᄋ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄋ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᅨᄎ ᆨᄆ ᅳ ᅩᄒ ᆼᄋ ᅧ ᅴᄋ ᅨᄎ ᆨᄉ ᅳ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋᄀ ᆷᄌ ᅥ ᆼᄒ ᅳ ᅡᄀ ᅵᄋ ᅱᄒ ᅢᄑ ᆼᄀ ᅧ ᆫᄌ ᅲ ᆯ ᅥ ᅢᄀ ᄃ ᆹᅩ ᅡ ᄋᄎ ᅡ (mean absolute error, MAE), ᄑ ᆼᄀ ᅧ ᆫᄌ ᅲ ᅦᄀ ᆸᄀ ᅩ ᆫᄋ ᅳ ᅩᄎ ᅡ (root mean squared error, RMSE), ᄀ ᅳᄅ ᅵᄀ ᅩ 2 ᆯᄌ ᅧ ᄀ ᆼᅨ ᅥ ᄀᄉ ᅮ (R )ᄀ ᅡᄉ ᅡᄋ ᆼᄃ ᅭ ᅬᄋ ᆻᄃ ᅥ ᅡ. ᆫᅮ ᅩ ᄂ ᆫ ᄆᄋ ᅴᄀ ᅮᄉ ᆼᄋ ᅥ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄃ ᅡ ᅡ. 2ᄌ ᆼᄋ ᅡ ᆫᄋ ᅳ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄒ ᆯᄋ ᅪ ᆼᄃ ᅭ ᆫᄋ ᅬ ᆫᄀ ᅧ ᅮᄇ ᆼᄇ ᅡ ᆸᄅ ᅥ ᆫᅳ ᅩ ᆯ ᄋᄃ ᅡᄅ ᅮᄀ ᅩ, 3ᄌ ᆼᄋ ᅡ ᆫᄋ ᅳ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅡᄋ ᆼᄃ ᅭ ᆫᄌ ᅬ ᅡᄅ ᅭ ᆨᄌ ᅳ ᄐ ᆼᄋ ᅵ ᆯᄀ ᅳ ᅵᄉ ᆯᄒ ᅮ ᅡᄋ ᆻᄃ ᅧ ᅡ. 4ᄌ ᆼᄋ ᅡ ᆫᄀ ᅳ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅳᄅ ᅩᄋ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᅨᄎ ᆨᄆ ᅳ ᅩᄒ ᆼᄋ ᅧ ᅴᄋ ᅨᄎ ᆨᄉ ᅳ ᆼᄂ ᅥ ᆼᄀ ᅳ ᆯᄀ ᅧ ᅪᄅ ᆯᄃ ᅳ ᅡᄅ ᆫᄃ ᅮ ᅡ. ᄆ ᅡᄌ ᅵᄆ ᆨᄋ ᅡ ᅳᄅ ᅩ 5ᄌ ᆼ ᅡ ᆫᄀ ᅳ ᄋ ᆯᄅ ᅧ ᆫᄆ ᅩ ᆾᅧ ᅵ ᆫ ᄋᄀ ᅮᄋ ᅴᅡ ᆫ ᄒᄀ ᅨᄌ ᆷᄋ ᅥ ᅦᄃ ᅢᄒ ᅡᄋ ᅧᄌ ᅦᄉ ᅵᄒ ᆫᄃ ᅡ ᅡ.. 2. 연구방법론 2.1. 랜덤포레스트 (randomforest, RF) ᆫᅥ ᅢ ᄅ ᆷ ᄃᄑ ᅩᄅ ᅦᄉ ᅳᄐ ᅳ (randomforest)ᄂ ᆫ ᄌ ᅳ ᅡᄅ ᅭᄅ ᆯ ᄌ ᅳ ᅩᄌ ᆯᄒ ᅥ ᅡᄂ ᆫ ᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᆫ ᄇ ᅵ ᅢᄀ ᆼ (bagging)ᄀ ᅵ ᅵᄇ ᆸᄋ ᅥ ᅳᄅ ᅩ ᄉ ᆼᄉ ᅢ ᆼᄃ ᅥ ᆫ ᄋ ᅬ ᅧᄅ ᅥ ᄀ ᅢ ᄋᄋ ᅴ ᅴᅡ ᄉᄀ ᆯᅥ ᅧ ᆼ ᄌᄂ ᅡᄆ ᅮᄆ ᅩᄒ ᆼᄋ ᅧ ᆯᄋ ᅳ ᆼᅡ ᅡ ᆼ ᄉᄇ ᆯᄀ ᅳ ᅵᄇ ᆸ (ensemble method)ᄋ ᅥ ᅳᄅ ᅩᄀ ᆯᄒ ᅧ ᆸᄒ ᅡ ᆫᄀ ᅡ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅵᄃ ᅡ. RFᄋ ᅴᄆ ᆨᄌ ᅩ ᆨᄋ ᅥ ᆫᄋ ᅳ ᅴ ᅡᄀ ᄉ ᆯᅥ ᅧ ᆼ ᄌᄂ ᅡᄆ ᅮᄆ ᅩᄒ ᆼᄋ ᅧ ᆯᄃ ᅳ ᅡᄉ ᅮᄆ ᆫᄃ ᅡ ᆯᄋ ᅳ ᅥᄃ ᅥᄌ ᆼᄒ ᅥ ᆨᄒ ᅪ ᆫᄋ ᅡ ᅨᄎ ᆨᄋ ᅳ ᆯᄒ ᅳ ᅡᄂ ᆫᄀ ᅳ ᆺᄋ ᅥ ᅵᄃ ᅡ (Yoo, 2015). Kim (2018)ᄀ ᅪ Breiman (2001)ᄋ ᅦ ᄄ ᅡᄅ ᅳᄆ ᆫ ᄋ ᅧ ᅴᄉ ᅡᄀ ᆯᅥ ᅧ ᆼ ᄌᄂ ᅡᄆ ᅮᄋ ᅴ ᄀ ᆼᄋ ᅧ ᅮᄂ ᆫ ᄌ ᅳ ᆨᄋ ᅡ ᆫ ᄑ ᅳ ᆫᄋ ᅧ ᅴ (bias)ᄋ ᅪ ᄏ ᆫ ᄇ ᅳ ᆫᄉ ᅮ ᆫᄋ ᅡ ᅳᄅ ᅩ ᄀ ᅪᄌ ᆨᄒ ᅥ ᆸ ᄒ ᅡ ᆯ ᄋ ᅡ ᅮᄅ ᅧᄀ ᅡ ᄋ ᆻᄋ ᅵ ᅳᄂ ᅡ, ᅴᄉ ᄋ ᅡᄀ ᆯᄌ ᅧ ᆼᄂ ᅥ ᅡᄆ ᅮ ᄆ ᅩᄒ ᆼᄃ ᅧ ᆯᄋ ᅳ ᅵ ᄉ ᅥᄅ ᅩ ᄌ ᅡᄀ ᅵ ᄉ ᆼᄀ ᅡ ᆫᄋ ᅪ ᆯ ᄄ ᅳ ᅡᄅ ᅳᄌ ᅵ ᄋ ᆭᄋ ᅡ ᆯᄉ ᅳ ᅮᄅ ᆨ ᄋ ᅩ ᅨᄎ ᆨ ᄋ ᅳ ᅩᄎ ᅡᄀ ᅡ ᄌ ᆨᄋ ᅡ ᅡᄌ ᅧ ᄋ ᅴᄉ ᅡᄀ ᆯᅥ ᅧ ᆼ ᄌᄂ ᅡᄆ ᅮᄋ ᅴ ᄉ ᅮ ᅡ ᄆ ᄀ ᆭᅡ ᅡ ᄋᄌ ᅧᄃ ᅩ ᄀ ᅪᄌ ᆨᄒ ᅥ ᆸ ᄒ ᅡ ᅡᄌ ᅵ ᄋ ᆭᄂ ᅡ ᆫᄃ ᅳ ᅡ. ᄒ ᅡᄌ ᅵᄆ ᆫ RFᄂ ᅡ ᆫ ᄀ ᅳ ᅨᄉ ᆫ ᄉ ᅡ ᅵᄀ ᆫᄋ ᅡ ᅵ ᄋ ᅩᄅ ᅢ ᄀ ᆯᄅ ᅥ ᅵᄂ ᆫ ᄃ ᅳ ᆫᄌ ᅡ ᆷᄃ ᅥ ᅩ ᄋ ᆻᄃ ᅵ ᅡ. ᄋ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫ ᅳ “randomForest” ᄑ ᅢᄏ ᅵᄌ ᅵᄋ ᅴ rf ᄒ ᆷᄉ ᅡ ᅮᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄋ ᆫᄀ ᅧ ᅮᄅ ᆯᄉ ᅳ ᅮᄒ ᆼᄒ ᅢ ᅡᄋ ᆻᄋ ᅧ ᅳᄆ ᅧ (Liawᄀ ᅪ Wiener, 2002), ᄎ ᅩᄆ ᅩᄉ ᅮᄂ ᆫ ᅳ mtryᄋ ᅵᄃ ᅡ..
(3) Insolation prediction using air pollutants and meteorological variables. 999. 2.2. 일반화가속모형 (Gradient boosting machine, GBM) Gradient boosting machine (GBM)ᄋ ᆫ Friedman (2001)ᄋ ᅳ ᅦ ᄋ ᅴᄒ ᅢ ᄀ ᅢᄇ ᆯᄃ ᅡ ᆫ ᄇ ᅬ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅵᄆ ᅧ, ᄇ ᅢᄀ ᆼᄋ ᅵ ᆯ ᄋ ᅳ ᅵᄋ ᆼ ᅭ ᄒ RFᄋ ᆫ ᅡ ᅪᄂ ᆫ ᄃ ᅳ ᅡᄅ ᅳᄀ ᅦ ᄇ ᅮᄉ ᅳᄐ ᆼ (boosting)ᄋ ᅵ ᆯ ᄒ ᅳ ᆯᄋ ᅪ ᆼᄒ ᅭ ᆫ ᄋ ᅡ ᆼᄉ ᅡ ᆼᄇ ᅡ ᆯ ᄀ ᅳ ᅵᄇ ᆸᄋ ᅥ ᅵᄃ ᅡ. GBMᄋ ᅴ ᄒ ᆨᄉ ᅢ ᆷᄋ ᅵ ᆯᄀ ᅡ ᅩᄅ ᅵᄌ ᆷᄋ ᅳ ᆫ adaptive ᅵ boosting (adaboost)ᄋ ᆫᄎ ᅳ ᅬᄌ ᆨᄒ ᅥ ᅪᄋ ᅦᄌ ᅮᄅ ᅩᄉ ᅡᄋ ᆼᄃ ᅭ ᅬᄂ ᆫᄀ ᅳ ᆼᄉ ᅧ ᅡᄒ ᅡᄀ ᆼᄇ ᅡ ᆸ (gradient descent)ᄋ ᅥ ᆯᄉ ᅳ ᆫᄉ ᅩ ᆯᄒ ᅵ ᆷᄉ ᅡ ᅮ (loss function)ᄋ ᅴᄎ ᅬᄉ ᅩᄒ ᅪᄋ ᅦᄌ ᆨᄋ ᅥ ᆼᄒ ᅭ ᆫᄇ ᅡ ᅮᄉ ᅳᄐ ᆼᄆ ᅵ ᅩᄒ ᆼᄋ ᅧ ᅵᄃ ᅡ (Friedman, 2001). ᄄ ᅩᄒ ᆫ GBMᄋ ᅡ ᆫᄌ ᅳ ᆼᄒ ᅥ ᆨᄃ ᅪ ᅩᄀ ᅡᄂ ᆽᄃ ᅡ ᅥᄅ ᅡᄃ ᅩᄋ ᅮᄉ ᆫᄆ ᅥ ᅩ ᆼᄋ ᅧ ᄒ ᆯᄉ ᅳ ᆼᅥ ᅢ ᆼ ᄉᄒ ᅡᄀ ᅩ, ᄋ ᅨᄎ ᆨᄋ ᅳ ᅩᄅ ᅲᄋ ᅦᄃ ᅢᄒ ᅢᄉ ᅥᄃ ᅡᄋ ᆷᄆ ᅳ ᅩᄃ ᆯᄋ ᅦ ᅵᄇ ᅩᄋ ᆫᄒ ᅪ ᅡᄂ ᆫᄀ ᅳ ᅪᄌ ᆼᄋ ᅥ ᆯᄇ ᅳ ᆫᄇ ᅡ ᆨᄉ ᅩ ᆯᄉ ᅵ ᅵᄒ ᆫᄃ ᅡ ᅡ. ᄋ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫ “gbm” ᄑ ᅳ ᅢ ᅵᄌ ᄏ ᅵᅴ ᄋ gbm ᄒ ᆷᄉ ᅡ ᅮᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᅧᄋ ᆫᄀ ᅧ ᅮᄅ ᆯᄉ ᅳ ᅮᄒ ᆼᄒ ᅢ ᅡᄋ ᆻᄋ ᅧ ᅳᄆ ᅧ (Ridgewayᄀ ᅪ Ridgeway, 2004), ᄎ ᅩᄆ ᅩᄉ ᅮᄂ ᆫ n.trees, ᅳ interaction.depth, shrinkage, n.minobsinnodeᄋ ᅵᄃ ᅡ. 2.3. Extreme gradient boosting machine (XGboost) Extreme gradient boosting machine (XGboost)ᄋ ᆫ Chenᄀ ᅳ ᅪ Guestrin (2016)ᄋ ᅦ ᄋ ᅴᄒ ᅢ ᄌ ᅦᄋ ᆫᄃ ᅡ ᆫ ᄇ ᅬ ᆼᄇ ᅡ ᆸ ᅥ ᄋᄅ ᅳ ᅩ, ᄀ ᅲᄆ ᅩᄀ ᅡ ᄏ ᆫ ᄌ ᅳ ᅡᄅ ᅭᄋ ᅦ ᄃ ᅢᄒ ᅢ ᄋ ᆫᄌ ᅡ ᆼᅥ ᅥ ᆼ ᄉᄀ ᅪ ᄒ ᆫᄅ ᅮ ᆫᄉ ᅧ ᆨᄃ ᅩ ᅩᄋ ᅴ ᅣ ᆼ ᄒᄉ ᆼᄋ ᅡ ᆯ ᄋ ᅳ ᅱᄒ ᅢ ᄌ ᅦᄋ ᆫᄃ ᅡ ᆫ ᄀ ᅬ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸ ᄀ ᅳ ᅵᄇ ᆸᄋ ᅥ ᅵᄃ ᅡ (Kim ᄃ ᆼ, ᅳ 2018). ᄄ ᅡᄅ ᅡᄉ ᅥ XGboostᄂ ᆫᄀ ᅳ ᅲᄆ ᅩᄀ ᅡᄏ ᆫᄌ ᅳ ᅡᄅ ᅭᄅ ᆯᄃ ᅳ ᅡᄅ ᅮᄂ ᆫᄃ ᅳ ᅦᄋ ᅦᄋ ᆫᄌ ᅡ ᆼᅥ ᅥ ᆼ ᄉᄋ ᅵᄂ ᇁᄀ ᅩ ᅩᄃ ᅡᄅ ᆫᄇ ᅳ ᅮᄉ ᅳᄐ ᆼᄆ ᅵ ᅩᄃ ᆯᄋ ᅦ ᅦᄇ ᅵᄒ ᅢᄒ ᆨ ᅡ ᆸᄉ ᅳ ᄉ ᆨᅩ ᅩ ᄃᄀ ᅡᄈ ᅡᄅ ᅳᄃ ᅡ (Park ᄃ ᆼ, 2020). ᄄ ᅳ ᅩᄒ ᆫ XGboostᄂ ᅡ ᆫᄆ ᅳ ᅩᄃ ᆯᄃ ᅦ ᆯᅳ ᅳ ᆯ ᄋᄐ ᆼᄒ ᅩ ᆸᄒ ᅡ ᆯᄄ ᅡ ᅢ, ᄆ ᅩᄃ ᆯᄇ ᅦ ᆯᄅ ᅧ ᅩᄉ ᅥᄅ ᅩᄃ ᅡᄅ ᆫᄀ ᅳ ᅡᄌ ᆼᄎ ᅮ ᅵ ᆯᄇ ᅳ ᄅ ᅮᅧ ᄋᄒ ᅡᄋ ᅧᄌ ᆼᄋ ᅮ ᅭᄃ ᅩᄀ ᅡᄂ ᇁᄋ ᅩ ᆫᄐ ᅳ ᅳᄅ ᅵᄆ ᅩᄃ ᆯᄋ ᅦ ᅦᄂ ᇁᄋ ᅩ ᆫᄌ ᅳ ᆷᄉ ᅥ ᅮᄅ ᆯᄇ ᅳ ᅮᄋ ᅧᄒ ᅡᄆ ᅧᄌ ᆨ, t ᄇ ᅳ ᆫᄍ ᅥ ᅢᄆ ᅩᄃ ᆯᄋ ᅦ ᅵᄀ ᅡᄌ ᅵᄂ ᆫᄀ ᅳ ᅡᄌ ᆼᄎ ᅮ ᅵᄂ ᆫ t-1 ᅳ ᆫᄍ ᅥ ᄇ ᅢᅴ ᄋᄋ ᅩᄅ ᅲᄋ ᅦᄄ ᅡᄅ ᅡᄉ ᅥᄀ ᆯᄌ ᅧ ᆼᄃ ᅥ ᆫᄃ ᅬ ᅡ (Oh ᄃ ᆼ, 2019). ᄒ ᅳ ᆨᄉ ᅡ ᆸᅳ ᅳ ᆯ ᄋᄋ ᅱᅡ ᆫ ᄒᄆ ᆨᄌ ᅩ ᆨᄒ ᅥ ᆷᄉ ᅡ ᅮᄂ ᆫᄀ ᅳ ᆫᄎ ᅪ ᆨᄎ ᅳ ᅵᅪ ᄋᄋ ᅨᄎ ᆨᄎ ᅳ ᅵᄀ ᆫᄋ ᅡ ᅴᄉ ᆫᄉ ᅩ ᆯᄒ ᅵ ᆷ ᅡ ᅮᅪ ᄉ ᄋᄌ ᆼᄀ ᅥ ᅲᄒ ᅪᄒ ᆼᄋ ᅡ ᅳᄅ ᅩᄀ ᅮᄉ ᆼᄃ ᅥ ᅬᄆ ᅧᄋ ᅵᄅ ᅥᄒ ᆫᄆ ᅡ ᆨᄌ ᅩ ᆨᄒ ᅥ ᆷᄉ ᅡ ᅮᄅ ᆯᄎ ᅳ ᅬᄉ ᅩᄒ ᅪᄒ ᅡᄂ ᆫᄀ ᅳ ᅡᄌ ᆼᄎ ᅮ ᅵᄅ ᆯᄀ ᅳ ᅮᄒ ᅡᄋ ᅧᄆ ᅩᄃ ᆯᄋ ᅦ ᆯᄒ ᅳ ᆨᄉ ᅡ ᆸᄒ ᅳ ᆫᄃ ᅡ ᅡ (Chenᄀ ᅪ Guestrin, 2016). ᄋ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫ “xgboost” ᄑ ᅳ ᅢᄏ ᅵᄌ ᅵᄋ ᅴ xgbLinear ᄒ ᆷᄉ ᅡ ᅮᄅ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄋ ᅧ ᅳᄆ ᅧ (Chen ᄃ ᆼ, 2015), ᅳ ᅩᄆ ᄎ ᅩᅮ ᄉᄂ ᆫ nrounds, lambda, alpha, etaᄀ ᅳ ᅡᄌ ᆫᄌ ᅩ ᅢᄒ ᆫᄃ ᅡ ᅡ. 2.4. k-겹 교차검증 (k-fold cross validation) K-ᄀ ᆸ ᄀ ᅧ ᅭᄎ ᅡᄀ ᆷᄌ ᅥ ᆼᄋ ᅳ ᆫ ᅮ ᅳ ᆫ ᄒᄅ ᆫᄌ ᅧ ᅡᄅ ᅭᄅ ᆯ kᄀ ᅳ ᅢᄅ ᅩ ᄇ ᆫᄒ ᅮ ᆯᄒ ᅡ ᅡᄀ ᅩ, ᄂ ᅡᄂ ᅮᄋ ᅥᄌ ᆫ ᄒ ᅵ ᆫᄅ ᅮ ᆫᄌ ᅧ ᅡᄅ ᅭ ᄌ ᆼ k-1ᄀ ᅮ ᅢᄅ ᆯ ᅮ ᅳ ᆫ ᄒᄅ ᆫᄌ ᅧ ᅡᄅ ᅭᄅ ᅩ ᄉ ᅡᄋ ᆼᄒ ᅭ ᅡᄀ ᅩ ᅡᄆ ᄂ ᅥᅵ ᄌ 1ᄀ ᅢᄋ ᅴ ᄌ ᅡᄅ ᅭᄅ ᆯ ᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᅧ ᄆ ᅩᄒ ᆼᄋ ᅧ ᅴ ᄉ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋ ᄀ ᆷᄌ ᅥ ᆼᄒ ᅳ ᅡᄂ ᆫ ᄇ ᅳ ᆼᄇ ᅡ ᆸᄋ ᅥ ᅵᄆ ᅧ, ᄃ ᆼᄇ ᅳ ᆫᄃ ᅮ ᆫ ᄉ ᅬ ᆺᄌ ᅮ ᅡᄆ ᆫᄏ ᅡ ᆷ ᄀ ᅳ ᆷᄌ ᅥ ᆼ ᄌ ᅳ ᅡᄅ ᅭᄀ ᅡ ᄌ ᆫᄌ ᅩ ᅢ ᅡᄀ ᄒ ᅵᄄ ᅢᄆ ᆫᄋ ᅮ ᅦ kᄇ ᆫᄋ ᅥ ᅴᄇ ᆫᄇ ᅡ ᆨᄉ ᅩ ᅮᄒ ᆼᄋ ᅢ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄀ ᆷᄌ ᅥ ᆼᄀ ᅳ ᅪᄌ ᆼᄋ ᅥ ᅵᄋ ᅵᄅ ᅮᄋ ᅥᄌ ᆫᄃ ᅵ ᅡ (Beaᄀ ᅪ Yoo, 2018). Molinaro (2005)ᄋ ᅪ Kohavi (1995)ᄋ ᅦᄋ ᅴᄒ ᅡᄆ ᆫᄇ ᅧ ᆫᄇ ᅡ ᆨᄃ ᅩ ᆫ k-ᄀ ᅬ ᆸᄀ ᅧ ᅭᄎ ᅡᄀ ᆷᄌ ᅥ ᆼᄋ ᅳ ᅵᄑ ᆫᄒ ᅧ ᆼᄋ ᅣ ᆯᄌ ᅳ ᆨᄀ ᅡ ᅦᄋ ᅲᄌ ᅵᄒ ᅡᄆ ᆫᄉ ᅧ ᅥᄎ ᅮᄌ ᆼᄋ ᅥ ᅴᄌ ᆼᄒ ᅥ ᆨᄃ ᅪ ᅩᄅ ᆯᄌ ᅳ ᆼᄀ ᅳ ᅡᄉ ᅵᄏ ᅵᄆ ᅧ, ᅧᄅ ᄋ ᅥᅩ ᆼ ᄌᄅ ᅲᄋ ᅴᄌ ᅡᄅ ᅭᄉ ᅦᄐ ᅳᄋ ᅦ 5-ᄀ ᆸᄀ ᅧ ᅭᄎ ᅡᄀ ᆷᄌ ᅥ ᆼᄋ ᅳ ᆫᄑ ᅳ ᆫᄒ ᅧ ᆼᄀ ᅣ ᅪᄇ ᆫᄉ ᅮ ᆫᄉ ᅡ ᅡᄋ ᅵᄋ ᅦᄀ ᅡᄌ ᆼᄌ ᅡ ᇂᄋ ᅩ ᆫᄌ ᅳ ᆯᄎ ᅥ ᆼᄌ ᅮ ᆷᄋ ᅥ ᆯᄌ ᅳ ᅮᄋ ᆻᄃ ᅥ ᅡᄀ ᅩᄇ ᆰᄒ ᅡ ᅧᄌ ᆻᄃ ᅧ ᅡ. 2.5. 평가척도) ᅵᄋ ᄋ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅥᄂ ᆫᄀ ᅳ ᅵᄉ ᆼᄀ ᅡ ᆫᄎ ᅪ ᆨᄌ ᅳ ᅡᄅ ᅭᄋ ᅪᄃ ᅢᄀ ᅵᄋ ᅩᄋ ᆷᄆ ᅧ ᆯᄌ ᅮ ᆯᄌ ᅵ ᅡᄅ ᅭᄅ ᆯᄐ ᅳ ᆼᄒ ᅩ ᆸᄒ ᅡ ᅡᄋ ᅧ RF, GBM, XGboostᄋ ᅴᄀ ᅵᄇ ᆸᅧ ᅥ ᆯ ᄇᄅ ᅩᄆ ᅩᄒ ᆼ ᅧ ᄀᄋ ᆫ ᅡ ᅨᅳ ᆨ ᄎᄉ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋᄇ ᅵᄀ ᅭᄒ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄑ ᆼᄀ ᅧ ᅡᄋ ᅴᄀ ᅵᄌ ᆫᄋ ᅮ ᅳᄅ ᅩᄂ ᆫ MAEᄋ ᅳ ᅪ RMSE ᄅ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄋ ᅧ ᅳᄆ ᅧ, ᄀ ᆨᄀ ᅡ ᆹᄋ ᅡ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄋ ᅡ ᅵ ᅨᄉ ᄀ ᆫᄃ ᅡ ᆫᄃ ᅬ ᅡ (Chaiᄀ ᅪ Draxler, 2014). v u P N N u1 X (yi − yb)2 1 X M AE = |yi − yb| , RM SE = t |yi − yb|, R2 = 1 − P , N i=1 N i=1 (yi − y)2 ᅧᄀ ᄋ ᅵᅥ ᄉ yi ᄂ ᆫᄀ ᅳ ᆫᄎ ᅪ ᆨᄀ ᅳ ᆹᄋ ᅡ ᅵᄆ ᅧ, ybᄋ ᆫᄋ ᅳ ᅨᄎ ᆨᄀ ᅳ ᆹ, yᄂ ᅡ ᆫᄑ ᅳ ᆼᄀ ᅧ ᆫᄀ ᅲ ᆹᄋ ᅡ ᅵᄃ ᅡ.. 3. 연구자료 ᅵᄋ ᄋ ᆫᄀ ᅧ ᅮᄂ ᆫᄀ ᅳ ᅵᄉ ᆼᄎ ᅡ ᆼᄀ ᅥ ᅵᄉ ᆼᄌ ᅡ ᅡᄅ ᅭᄀ ᅢᄇ ᆼᄑ ᅡ ᅩᄐ ᆯ (https://data.kma.go.kr)ᄋ ᅥ ᅦᄉ ᅥᅦ ᄌᄀ ᆼᄒ ᅩ ᅡᄂ ᆫᄌ ᅳ ᆼᄀ ᅩ ᆫᄀ ᅪ ᅵᄉ ᆼᄀ ᅡ ᆫᄎ ᅪ ᆨᄉ ᅳ ᅩ (automated synoptic observing system, ASOS)ᄋ ᅦᄉ ᅥ ᄉ ᅮᄌ ᆸᄃ ᅵ ᆫ 2015ᄂ ᅬ ᆫ 1ᄋ ᅧ ᆯ 1ᄋ ᅯ ᆯᅮ ᅵ ᄇᄐ ᅥ 2019ᄂ ᆫ 12ᄋ ᅧ ᆯ 31ᄋ ᅯ ᆯᄁ ᅵ ᅡᄌ ᅵ.
(4) 1000. Yeongeun Hwang · Dayoung Kang · Myunghwan Na · Sanghoo Yoon. 5ᄂ ᆫᄀ ᅧ ᆫᄋ ᅡ ᅴᄌ ᅡᄅ ᅭᄅ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄋ ᆫᄀ ᅧ ᅮᄋ ᅦᄉ ᅡᄋ ᆼᄃ ᅭ ᆫᄀ ᅬ ᅵᄉ ᆼᄇ ᅡ ᆫᄉ ᅧ ᅮᄂ ᆫᄋ ᅳ ᆯᄉ ᅵ ᅡᄅ ᆼ, ᄀ ᅣ ᅵᄋ ᆫ (temp), ᄀ ᅩ ᆼᄉ ᅡ ᅮᄅ ᆼ (prec), ᄉ ᅣ ᆼᄃ ᅡ ᅢᄉ ᆸᄃ ᅳ ᅩ (rh), ᅳ ᆼ ᄌᄀ ᅵᄋ ᆸ (vapor), ᄒ ᅡ ᆫᄌ ᅧ ᅵᄀ ᅵᄋ ᆸ (pressure), ᄒ ᅡ ᅢᄆ ᆫᄀ ᅧ ᅵᄋ ᆸ (spongy), ᄋ ᅡ ᅵᄉ ᆯᄌ ᅳ ᆷ (dew), ᄋ ᅥ ᆯᄌ ᅵ ᅩ (sunlight), ᄀ ᅡᄌ ᅩᄉ ᅵ ᆫ (duration), ᄌ ᅡ ᄀ ᆫᄋ ᅥ ᆫᄅ ᅮ ᆼ (cloud), ᄌ ᅣ ᆼᄒ ᅮ ᅡᄎ ᆼᅮ ᅳ ᆫ ᄋᄅ ᆼ (cloud b)ᄋ ᅣ ᅵᄃ ᅡ. ᄀ ᅵᄋ ᆫ, ᄀ ᅩ ᆼᄉ ᅡ ᅮᄅ ᆼ, ᄉ ᅣ ᆼᄃ ᅡ ᅢᄉ ᆸᄃ ᅳ ᅩ, ᄌ ᆼᄀ ᅳ ᅵᄋ ᆸ, ᄒ ᅡ ᆫᄌ ᅧ ᅵᄀ ᅵᄋ ᆸ, ᅡ ᅢᄆ ᄒ ᆫᅵ ᅧ ᄀᄋ ᆸᄋ ᅡ ᆫᄑ ᅳ ᆼᄀ ᅧ ᆫᄀ ᅲ ᅪᄎ ᅬᄉ ᅩᄆ ᆾᄎ ᅵ ᅬᄃ ᆺᄀ ᅢ ᆹᄋ ᅡ ᆯᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄎ ᆼ 95ᄀ ᅩ ᅢᄋ ᅴ ASOS ᄌ ᆼᄋ ᅮ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᆯ 4ᄂ ᅳ ᆫᄋ ᅧ ᅵᄉ ᆼᄀ ᅡ ᆫᄎ ᅪ ᆨᄃ ᅳ ᆫ 42ᄀ ᅬ ᅢ ᅩᄅ ᄉ ᆯᄉ ᅳ ᆫᅥ ᅥ ᆼ ᄌᄒ ᅡᄋ ᅧᄋ ᆫᄀ ᅧ ᅮᄅ ᆯᄉ ᅳ ᅮᄒ ᆼᄒ ᅢ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᆾᄋ ᅵ ᄇ ᆯᄉ ᅳ ᆫᅡ ᅡ ᆫ ᄅᄉ ᅵᄏ ᅧᄋ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᅦᄋ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆯᄆ ᅳ ᅵᄎ ᆯᄉ ᅵ ᅮᄋ ᆻᄂ ᅵ ᆫᄃ ᅳ ᅢᄀ ᅵᄋ ᅩᄋ ᆷᄆ ᅧ ᆯᄌ ᅮ ᆯᄌ ᅵ ᅡᄅ ᅭᄃ ᅩᄀ ᅵᄉ ᆼᄌ ᅡ ᅡᄅ ᅭᅪ ᄋᄃ ᆼᄋ ᅩ ᆯᄀ ᅵ ᅵᄀ ᆫᄋ ᅡ ᆫ 2015ᄂ ᅵ ᆫ ᅧ 1ᄋ ᆯ 1ᄋ ᅯ ᆯᄇ ᅵ ᅮᄐ ᅥ 2019ᄂ ᆫ 12ᄋ ᅧ ᆯ 31ᄋ ᅯ ᆯᄁ ᅵ ᅡᄌ ᅵᄋ ᅦᄋ ᅥᄏ ᅩᄅ ᅵᄋ ᅡ (https://www.airkorea.or.kr)ᄅ ᅩᄇ ᅮᄐ ᅥᄉ ᅮᄌ ᆸᄒ ᅵ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄀ ᆫ ᅪ ᆨᄃ ᅳ ᄎ ᆫᄃ ᅬ ᅢᄀ ᅵᄋ ᅩᄋ ᆷᄆ ᅧ ᆯᄌ ᅮ ᆯᄂ ᅵ ᆼᄃ ᅩ ᅩᄂ ᆫᄋ ᅳ ᅩᄌ ᆫ (O3), ᄆ ᅩ ᅵᄉ ᅦᄆ ᆫᄌ ᅥ ᅵ (PM10), ᄎ ᅩᄆ ᅵᄉ ᅦᄆ ᆫᄌ ᅥ ᅵ (PM25), ᄋ ᅵᄉ ᆫᄒ ᅡ ᅪᄌ ᆯᄉ ᅵ ᅩ (NO2), ᄋ ᆯ ᅵ ᆫᄒ ᅡ ᄉ ᅪᅡ ᆫ ᄐᄉ ᅩ (CO), ᄋ ᅵᄉ ᆫᄒ ᅡ ᅪᄒ ᆼ (SO2), ᄋ ᅪ ᅵᄉ ᆫᄒ ᅡ ᅪᄌ ᆯᄉ ᅵ ᅩ (NO2)ᄋ ᅵᄃ ᅡ. ᄃ ᅢᄀ ᅵᄋ ᅩᄋ ᆷᄆ ᅧ ᆯᄌ ᅮ ᆯ ᄀ ᅵ ᆫ ᄉ ᅡ ᆼᄀ ᅡ ᆫ ᄇ ᅪ ᆫᄉ ᅮ ᆨᄒ ᅥ ᆫ ᄀ ᅡ ᆯᄀ ᅧ ᅪᄂ ᆫ Taᅳ ble 3.1ᄋ ᅵᄃ ᅡ. PM10ᄋ ᅪ PM25 ᄀ ᆫ ᄂ ᅡ ᇁᄋ ᅩ ᆫ ᄉ ᅳ ᆼᄀ ᅡ ᆫᄉ ᅪ ᆼ (r=0.803)ᄋ ᅥ ᅵ ᄌ ᆫᄌ ᅩ ᅢᄒ ᅡᄀ ᅩ, COᄋ ᅪ NO ᄀ ᆫ ᄉ ᅡ ᆼᄀ ᅡ ᆫᄉ ᅪ ᆼᄋ ᅥ ᅵ ᄋ ᆻᄃ ᅵ ᅡ (r=0.608). Table 3.1 The correlation pearson coefficient between air pollutants CO O3 NO2 PM10 PM25. SO2 0.291 -0.040 0.337 0.291 0.304. CO -0.321 0.608 0.455 0.559. O3. NO2. PM10. PM25. -0.455 0.026 -0.030. 0.441 0.497. 0.803. -. 도 ᅢ ᄃᄉ ᅵᄅ ᆯᄌ ᅳ ᆼᄉ ᅮ ᆷᄋ ᅵ ᅳᄅ ᅩᄉ ᆯᄎ ᅥ ᅵᄋ ᆫᄋ ᅮ ᆼᄌ ᅧ ᆼᄋ ᅮ ᆫᄃ ᅵ ᅢᄀ ᅵᄋ ᅩᄋ ᆷᄆ ᅧ ᆯᄌ ᅮ ᆯᄀ ᅵ ᆫᄎ ᅪ ᆨᄉ ᅳ ᅩ (air pollutants monitoring sites, AMS)ᄂ ᆫ ᅳ ᆼ 405ᄀ ᅩ ᄎ ᅢᄉ ᅩᄋ ᅵᄃ ᅡ. ᄉ ᆫᅥ ᅵ ᆯ ᄉᄃ ᅬᄀ ᅥᄂ ᅡᄉ ᆫᄉ ᅦ ᅥᄋ ᅴᄋ ᅩᄅ ᅲᄅ ᅩᄃ ᅢᄀ ᅵᄋ ᅩᄋ ᆷᄆ ᅧ ᆯᄌ ᅮ ᆯᄀ ᅵ ᆫᄎ ᅪ ᆨᄋ ᅳ ᅵᄃ ᅬᄌ ᅵᄋ ᆭᄋ ᅡ ᆫᄀ ᅳ ᆫᄎ ᅪ ᆨᄉ ᅳ ᅩᄀ ᅡᄌ ᆫᄌ ᅩ ᅢᄒ ᅡᄋ ᅧ 1,800ᄋ ᆯ ᅵ ᅵᄉ ᄋ ᆼᅵ ᅡ ᄋᄀ ᆫ ᆫᄎ ᅪ ᆨᄃ ᅳ ᆫ 225ᄀ ᅬ ᅢᄋ ᅴᄀ ᆫᄎ ᅪ ᆨᄉ ᅳ ᅩᄆ ᆫᄉ ᅡ ᆫᄌ ᅥ ᆼᄒ ᅥ ᅡᄋ ᅧᄋ ᆫᄀ ᅧ ᅮᄅ ᆯᄉ ᅳ ᅮᄒ ᆼᄒ ᅢ ᅡᄋ ᆻᄃ ᅧ ᅡ. ASOSᄋ ᅪ AMSᄋ ᅴᄀ ᆼᄀ ᅩ ᆫᄇ ᅡ ᆫᄑ ᅮ ᅩᄅ ᆯᄉ ᅳ ᅵᄀ ᆨᄒ ᅡ ᅪᄒ ᅡᄆ ᆫ Fig 3.1ᄋ ᅧ ᅵᄃ ᅡ. ᄀ ᅵᄉ ᆼᄀ ᅡ ᆫᄎ ᅪ ᆨᄉ ᅳ ᅩᅪ ᄋᄃ ᅢᄀ ᅵᄋ ᅩᄋ ᆷᄀ ᅧ ᆫᄎ ᅪ ᆨᄉ ᅳ ᅩᄋ ᅴᄋ ᅱᄎ ᅵᄂ ᆫᄃ ᅳ ᆼ ᅩ ᆯᄒ ᅵ ᄋ ᅡᄌ ᅵᄋ ᆭᄋ ᅡ ᅡᄀ ᆼᄀ ᅩ ᆫᄌ ᅡ ᆨᄋ ᅥ ᅵᄌ ᆯᅥ ᅵ ᆼ ᄉᄆ ᆫᄌ ᅮ ᅦᄀ ᅡᄇ ᆯᄉ ᅡ ᆼᄒ ᅢ ᆫᄃ ᅡ ᅡ. ᄋ ᅵᄂ ᆫᄀ ᅳ ᆼᄀ ᅩ ᆫᄂ ᅡ ᅢᄉ ᆸᄋ ᅡ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢᄒ ᅢᄀ ᆯᄒ ᅧ ᆯᄉ ᅡ ᅮᄋ ᆻᄋ ᅵ ᅳᄂ ᅡ ASOSᄋ ᅪ AMS ᆫ ᄀ ᅡ ᄀ ᅥᅵ ᄅᄀ ᅡ ᄆ ᆫ ᄀ ᅥ ᆼᄋ ᅧ ᅮ ᄇ ᆯᄒ ᅮ ᆨᄉ ᅪ ᆯᅥ ᅵ ᆼ ᄉᄋ ᅵ ᄂ ᇁᄋ ᅩ ᅳᄆ ᅳᄅ ᅩ ᄎ ᅬᄀ ᆫᄌ ᅳ ᆸᄋ ᅥ ᅵᄋ ᆺᄇ ᅮ ᆸᄋ ᅥ ᆯ ᄉ ᅳ ᅡᄋ ᆼᄒ ᅭ ᅡᄋ ᅧ ᄋ ᆫᄀ ᅧ ᅮᄅ ᆯ ᄌ ᅳ ᆫᄒ ᅵ ᆼᄒ ᅢ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄋ ᅮᄉ ᆫ AMSᄋ ᅥ ᅪ ASOS ᄀ ᆫᄀ ᅡ ᅥᄅ ᅵᄋ ᅴᄇ ᆫᄑ ᅮ ᅩᄂ ᆫ Fig 3.2ᄋ ᅳ ᅵᄃ ᅡ. AMSᄋ ᅪ ASOS ᄉ ᅡᄋ ᅵᄀ ᅥᄅ ᅵᄋ ᅴᄌ ᆼᄋ ᅮ ᆼᄀ ᅡ ᆹᄋ ᅡ ᆫᄋ ᅳ ᆨ 5kmᄋ ᅣ ᅵᄃ ᅡ. ᄋ ᆫᄀ ᅧ ᅮᄋ ᅴᄑ ᆫᄋ ᅧ ᅴ ᆼᄋ ᅥ ᄉ ᆯᅱ ᅳ ᄋᄒ ᅢ 5km ᄀ ᅥᄅ ᅵᄋ ᅴᄃ ᅢᄀ ᅵᄋ ᅩᄋ ᆷᄆ ᅧ ᆯᄌ ᅮ ᆯᄂ ᅵ ᆼᄃ ᅩ ᅩᄂ ᆫᄃ ᅳ ᆼᄋ ᅩ ᆯᄒ ᅵ ᅡᄃ ᅡᄀ ᅩᄀ ᅡᄌ ᆼᄒ ᅥ ᅡᄀ ᅩ ASOS 22ᄀ ᅢᄉ ᅩᄅ ᆯᄉ ᅳ ᆫᅥ ᅥ ᆼ ᄌᄒ ᅡᄋ ᅧᄋ ᆫᄀ ᅧ ᅮᄅ ᆯᄉ ᅳ ᅮ ᆼᄒ ᅢ ᄒ ᅡᅧ ᆻ ᄋᄃ ᅡ.. Figure 3.1 The spatial distribution of weather station and air monitoring sites. ᅵᄀ ᄉ ᆫᄋ ᅡ ᅦᄄ ᅡᄅ ᆫᄋ ᅳ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᅴᄇ ᆫᄑ ᅮ ᅩᄅ ᆯᄉ ᅳ ᅵᄀ ᆨᄒ ᅡ ᅪᄒ ᅡᄆ ᆫ Fig 3.3ᄋ ᅧ ᅵᄃ ᅡ. Lee ᄃ ᆼ (2017)ᄋ ᅳ ᅴᄋ ᆫᄀ ᅧ ᅮᄋ ᅪᄀ ᇀᄋ ᅡ ᅵᄋ ᆯᄌ ᅵ ᅩᄉ ᅵᄀ ᆫᄋ ᅡ ᅵᄀ ᆯ ᅵ ᅩᄇ ᄀ ᅵᅡ ᄀᄂ ᅢᄅ ᅵᄌ ᅵᄋ ᆭᄂ ᅡ ᆫᄇ ᅳ ᆷ (4∼5ᄋ ᅩ ᆯ)ᄋ ᅯ ᅴᄋ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᅵᄂ ᇁᄋ ᅩ ᆻᄃ ᅡ ᅡ. ᆯᄃ ᅵ ᄋ ᆫᄋ ᅡ ᅱᄋ ᆯᄉ ᅵ ᅡᄅ ᆼᄌ ᅣ ᅡᄅ ᅭᄂ ᆫᄉ ᅳ ᅵᄀ ᅨᄋ ᆯᄌ ᅧ ᅡᄅ ᅭᄋ ᅵᄆ ᅳᄅ ᅩᄉ ᅵᄀ ᆫᄋ ᅡ ᅦᄄ ᅡᄅ ᆫᄎ ᅳ ᅮᄉ ᅦᄉ ᆼᄀ ᅥ ᅪᄀ ᅨᄌ ᆯᄉ ᅥ ᆼᄋ ᅥ ᆯᄒ ᅳ ᆨᄋ ᅪ ᆫᄒ ᅵ ᅡᄆ ᆫ Fig 3.4ᄋ ᅧ ᅵᄃ ᅡ. ᄀ ᅨ.
(5) Insolation prediction using air pollutants and meteorological variables. 1001. Figure 3.2 The distance between weather stations and air monitoring sites (unit: km). Figure 3.3 Monthly and hourly solar radiation in Korea (kM h/m2 ). 저 ᆯ ᅥ ᆼ 시 ᄋᄄ ᅮᄅ ᆺᄋ ᅧ ᅵᄇ ᅩᄋ ᅵᄆ ᅧᄋ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᅵᄂ ᆽᄋ ᅡ ᆫᄀ ᅳ ᅧᄋ ᆯᄎ ᅮ ᆯᄋ ᅥ ᅦᄇ ᅵᄒ ᅢᄇ ᆷᄀ ᅩ ᅪᄋ ᅧᄅ ᆷᄎ ᅳ ᆯᄋ ᅥ ᆯᄉ ᅵ ᅡᄅ ᆼᄀ ᅣ ᆫᄎ ᅪ ᆨᄋ ᅳ ᅴᄇ ᆫᄃ ᅧ ᆼᄉ ᅩ ᆼᄋ ᅥ ᅵᄏ ᅳᄃ ᅡ. ᄋ ᆯᄉ ᅵ ᅡᄅ ᆼ ᅣ ᅴᄉ ᄋ ᅵᄀ ᅨᄋ ᆯᄌ ᅧ ᆨᄋ ᅥ ᅭᄋ ᆫᄋ ᅵ ᅦᄄ ᅡᄅ ᆫᄋ ᅳ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆯᄌ ᅳ ᅦᄀ ᅥᄒ ᅡᄀ ᅵᄋ ᅱᄒ ᅢ ASOS ᄇ ᆯ ARIMA ᄆ ᅧ ᅩᄒ ᆼᄋ ᅧ ᆯᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ.. Figure 3.4 ARIMA model for daily solar radiation(left) and residuals(right). 4. 연구결과 ᅵᄉ ᄀ ᆼ ᄆ ᅡ ᆾ ᄃ ᅵ ᅢᄀ ᅵᄋ ᅩᄋ ᆷ ᄌ ᅧ ᅡᄅ ᅭᄋ ᅦ ᄄ ᅡᄅ ᆫ ᄋ ᅳ ᆯ ᄃ ᅵ ᆫᄋ ᅡ ᅱ ᄋ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᆯ ᄋ ᅳ ᅨᄎ ᆨᄒ ᅳ ᅡᄀ ᅵ ᄋ ᅱᄒ ᅡᄋ ᅧ RF, GBM, XGboostᄋ ᅴ ᄆ ᅩᄒ ᆼ ᅧ ᄋ ᄀ ᆯ ᅳ ᅮᄎ ᆨᄒ ᅮ ᅡᄋ ᅧ ᄇ ᆫᄉ ᅮ ᆨᄋ ᅥ ᆯ ᄌ ᅳ ᆫᄒ ᅵ ᆼᄒ ᅢ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄌ ᆫᄎ ᅥ ᅦ ᄌ ᅡᄅ ᅭᄋ ᅦ ᄃ ᅢᄒ ᅡᄋ ᅧ 80%ᄋ ᅴ ᄒ ᆫᄅ ᅮ ᆫ ᄌ ᅧ ᅡᄅ ᅭᄋ ᅪ 20%ᄋ ᅴ ᄀ ᆷᄌ ᅥ ᆼᄋ ᅳ ᆼ ᄌ ᅭ ᅡᄅ ᅭᄅ ᅩ 2 ᆯᄌ ᅥ ᄉ ᆼᄒ ᅥ ᆫ ᄒ ᅡ ᅮ MAE, RMSE, R ᄅ ᆯ ᄒ ᅳ ᆯᄋ ᅪ ᆼᄒ ᅭ ᅡᄋ ᅧ ᄌ ᅦᄋ ᆫᄃ ᅡ ᆫ ᄆ ᅬ ᅩᄒ ᆼᄋ ᅧ ᅴ ᄉ ᆼᄂ ᅥ ᆼᄋ ᅳ ᆯ ᄌ ᅳ ᆼᄅ ᅥ ᆼᄌ ᅣ ᆨᄋ ᅥ ᅳᄅ ᅩ ᄇ ᅵᄀ ᅭᄒ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄆ ᅩᄒ ᆼ ᄀ ᅧ ᆫ ᅡ 5ᄀ ᆸ ᄀ ᅧ ᅭᄎ ᅡ ᄀ ᆷᄌ ᅥ ᆼᄋ ᅳ ᅴ ᄋ ᅨᄎ ᆨᄉ ᅳ ᆼᄂ ᅥ ᆼᄋ ᅳ ᆫ Table 4.1ᄋ ᅳ ᅵᄃ ᅡ. ᄎ ᅬᄌ ᆨ ᄎ ᅥ ᅩᄆ ᅩᄉ ᅮᄅ ᆯ ᄉ ᅳ ᆫᅥ ᅥ ᆼ ᄌᄒ ᅡᄀ ᅵ ᄋ ᅱᄒ ᅢ random ᄉ ᅥᄎ ᅵᄅ ᆯ ᄋ ᅳ ᅵᄋ ᆼᄒ ᅭ ᅡ ᆻᄃ ᅧ ᄋ ᅡ. RFᄂ ᆫ ᄀ ᅳ ᆨ ᄂ ᅡ ᅩᄃ ᅳᄋ ᅦᄉ ᅥ ᄋ ᆷᄋ ᅵ ᅴᄅ ᅩ ᄀ ᅩᄅ ᅧᄃ ᆯ ᄇ ᅬ ᆫᄉ ᅧ ᅮ ᄌ ᅩᄒ ᆸᄋ ᅡ ᅵ 22ᄀ ᅢᄋ ᆯ ᄄ ᅵ ᅢ ᄉ ᆼᄂ ᅥ ᆼᄋ ᅳ ᅵ ᄋ ᅮᄉ ᅮᄒ ᅡᄋ ᆻᄃ ᅧ ᅡ (MAE=1.368, RMSE=1.818, R2 =0.854). GBMᄋ ᆫᄀ ᅳ ᆨᄐ ᅡ ᅳᄅ ᅵᄋ ᅦᄉ ᅥ 10ᄀ ᅢᄂ ᅩᄃ ᅳᄐ ᅳᄅ ᅵᄅ ᅩᄇ ᆫᄒ ᅮ ᆯᄒ ᅡ ᅡᄋ ᅧᄎ ᅬᄉ ᅩ 14ᄒ ᅬᄀ ᆫᄎ ᅪ ᆯᅡ ᅡ ᆯ ᄒᄄ ᅢᄌ ᆨ ᅥ ᆸᄒ ᅡ ᄒ ᅡᄃ ᅡ (MAE=1.135, RMSE=1.515, R2 =0.897). XGboostᄂ ᆫ ᄒ ᅳ ᆨᄉ ᅡ ᆸᅲ ᅳ ᆯ ᄅ 0.135, ᄎ ᅬᄉ ᅩ ᄉ ᆫᄉ ᅩ ᆯ ᄀ ᅵ ᆷᄉ ᅡ ᅩ 9.959, ᆨ ᄐ ᅡ ᄀ ᅳᅵ ᄅᄋ ᅴ ᄎ ᅬᄃ ᅢ ᄀ ᇁᄋ ᅵ ᅵ 5, ᄆ ᅩᄃ ᆫ ᄀ ᅳ ᆫᄎ ᅪ ᆨᄎ ᅳ ᅵᄋ ᅴ ᄎ ᅬᄉ ᅩ ᄀ ᅡᄌ ᆼᄎ ᅮ ᅵ ᄒ ᆸ 15ᄋ ᅡ ᆯ ᄄ ᅵ ᅢ ᄋ ᅨᄎ ᆨᄉ ᅳ ᆼᄂ ᅥ ᆼᄋ ᅳ ᅵ ᄂ ᇁᄋ ᅩ ᆻᄃ ᅡ ᅡ (MAE=1.181,.
(6) 1002. Yeongeun Hwang · Dayoung Kang · Myunghwan Na · Sanghoo Yoon. RMSE=1.574, R2 =0.889). ᄆ ᅩᄒ ᆼᄀ ᅧ ᆫᄋ ᅡ ᅨᄎ ᆨᄉ ᅳ ᆼᄂ ᅥ ᆼᅳ ᅳ ᆯ ᄋ 5ᄀ ᆸᄀ ᅧ ᅭᄎ ᅡᄀ ᆷᄌ ᅥ ᆼᄋ ᅳ ᅳᄅ ᅩᄇ ᅵᄀ ᅭᄒ ᆫᄀ ᅡ ᆯᄀ ᅧ ᅪ GBMᄋ ᆯᄌ ᅳ ᆨᄋ ᅥ ᆼᄒ ᅭ ᆫᄆ ᅡ ᅩᄒ ᆼᄋ ᅧ ᅵ ᆼᄃ ᅡ ᄉ ᅢᅥ ᆨ ᄌᄋ ᅳᄅ ᅩᄋ ᆯᄋ ᅵ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᆯᄌ ᅳ ᆯᄋ ᅡ ᅨᄎ ᆨᄒ ᅳ ᅡᄋ ᆻᄃ ᅧ ᅡ. Table 4.1 The result of 5-fold cross validation RF GBM XGboost. MAE (SD) 1.368 (0.017) 1.135 (0.011) 1.181(0.021). RMSE (SD) 1.818 (0.022) 1.515 (0.034) 1.574 (0.036). R2 0.854 0.897 0.889. (SD) (0.005) (0.004) (0.006). ᄒᄅ ᆫ ᅮ ᆫᄌ ᅧ ᅡᄅ ᅭᄋ ᅦᄃ ᅢᄒ ᅢᄎ ᅬᄌ ᆨᄋ ᅥ ᅴ RF, GBM, XGboostᄅ ᆯᄌ ᅳ ᆨᄋ ᅥ ᆼᄒ ᅭ ᅡᄋ ᅧᄀ ᆷᄌ ᅥ ᆼᄋ ᅳ ᆼᄌ ᅭ ᅡᄅ ᅭᄋ ᅴᄋ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᆯᄋ ᅳ ᅨᄎ ᆨᄒ ᅳ ᆫᅧ ᅡ ᆯ ᄀᄀ ᅪᄂ ᆫ ᅳ Table 4.2ᄋ ᅵᄃ ᅡ. 5ᄀ ᆸ ᄀ ᅧ ᅭᄎ ᅡᄀ ᆷᄌ ᅥ ᆼ ᄀ ᅳ ᆯᄀ ᅧ ᅪᄋ ᅪ ᄃ ᆯᄅ ᅡ ᅵ RFᄋ ᅴ ᄋ ᅨᄎ ᆨᄅ ᅳ ᆨᄋ ᅧ ᅵ ᄂ ᇁᄃ ᅩ ᅡ. ᄋ ᆯᄉ ᅵ ᅡᄅ ᆼ ᄋ ᅣ ᅨᄎ ᆨᄋ ᅳ ᆯ ᄋ ᅳ ᅱᄒ ᆫ GBMᄀ ᅡ ᅪ XGboostᄂ ᆫᄀ ᅳ ᅪᄃ ᅢᄌ ᆨᄒ ᅥ ᆸᄋ ᅡ ᅴᄆ ᆫᄌ ᅮ ᅦᄌ ᆷᄋ ᅥ ᅵᄋ ᆻᄃ ᅵ ᅡ. Table 4.2 The result of test data RF GBM XGboost. MAE 0.532 0.778 0.793. RMSE 0.718 1.1015 1.041. R2 0.979 0.954 0.952. ᆯᄉ ᅵ ᄋ ᅡᄅ ᆼᄋ ᅣ ᅨᄎ ᆨᄋ ᅳ ᅦᄋ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆯᄆ ᅳ ᅵᄎ ᅵᄂ ᆫᄇ ᅳ ᆫᄉ ᅧ ᅮᄋ ᅴᄌ ᆼᄋ ᅮ ᅭᄃ ᅩᄂ ᆫ Table 4.3ᄋ ᅳ ᅵᄃ ᅡ. RF ᄀ ᅵᄌ ᆫᄋ ᅮ ᅳᄅ ᅩᄌ ᆼᄅ ᅥ ᆯᄃ ᅧ ᅬᄋ ᅥᄋ ᆻᄋ ᅵ ᅳᄂ ᅡᄆ ᅩᄃ ᆫ ᅳ ᄆᄒ ᅩ ᆼᅦ ᅧ ᄋᄉ ᅥᄋ ᆯᄌ ᅵ ᅩᄉ ᅵᄀ ᆫ (sunlight)ᄋ ᅡ ᅵᄌ ᆯᄃ ᅥ ᅢᄌ ᆨᄋ ᅥ ᅳᄅ ᅩᄋ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᅨᄎ ᆨᄋ ᅳ ᅦᄆ ᅢᄋ ᅮᄌ ᆼᄋ ᅮ ᅭᄒ ᆫᄇ ᅡ ᆫᄉ ᅧ ᅮᄋ ᅵᄃ ᅡ. ᄋ ᅵᄋ ᅬᄋ ᅦᄃ ᅩᄀ ᅡᄌ ᅩᄉ ᅵᄀ ᆫ ᅡ (duration), ᄀ ᆼᄉ ᅡ ᅮᄅ ᆼ (prec), ᄀ ᅣ ᅮᄅ ᆷ (cloud), ᄋ ᅳ ᅱᄃ ᅩ (lat) ᄃ ᆼᄋ ᅳ ᅴᄉ ᆫᄋ ᅮ ᅳᄅ ᅩᄌ ᆼᄋ ᅮ ᅭᄃ ᅩᄀ ᅡᄂ ᇁᄋ ᅩ ᆻᄃ ᅡ ᅡ. Jung ᄃ ᆼ (2011)ᄀ ᅳ ᅪ Won ᅳ ᆼ ᄃ (2011)ᄋ ᆫᄀ ᅳ ᅮᄅ ᆷᄋ ᅳ ᅳᄅ ᅩᄋ ᆫᄒ ᅵ ᆫᄋ ᅡ ᆯᄉ ᅵ ᅡᄅ ᆼᄀ ᅣ ᆷᄉ ᅡ ᅩᄅ ᆯᄆ ᅳ ᅩᄒ ᆼᄋ ᅧ ᅦᄀ ᅩᄅ ᅧᄒ ᅡᄋ ᆻᄋ ᅧ ᅳᄂ ᅡᄇ ᆫᄋ ᅩ ᆫᄀ ᅧ ᅮᄀ ᆯᄀ ᅧ ᅪ, ᄋ ᆫᄅ ᅮ ᆼᄋ ᅣ ᆫᄋ ᅳ ᆯᄋ ᅵ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᅨ ᆨᄋ ᅳ ᄎ ᅦᄏ ᆫᄋ ᅳ ᆼᄒ ᅧ ᆼᄋ ᅣ ᆯᄆ ᅳ ᅵᄎ ᅵᄌ ᅵᄋ ᆭᄂ ᅡ ᆫᄃ ᅳ ᅡ. ᄋ ᅵᄂ ᆫᄉ ᅳ ᆫᄒ ᅥ ᆼᅧ ᅢ ᆫ ᄋᄀ ᅮᄋ ᆫ Kim (2019)ᄋ ᅵ ᅴᄀ ᆯᄀ ᅧ ᅪᄋ ᅪᄋ ᅲᄉ ᅡᄒ ᅡᄃ ᅡ. Lee ᄃ ᆼ (2017)ᄀ ᅳ ᅡᄀ ᅩ ᅧᄒ ᄅ ᆫᅳ ᅡ ᆸ ᄉᄃ ᅩᅪ ᄋᄆ ᅵᄉ ᅦᄆ ᆫᄌ ᅥ ᅵᄂ ᆼᄃ ᅩ ᅩᄃ ᅩᄋ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᅨᄎ ᆨᄋ ᅳ ᅦᄌ ᆼᄋ ᅮ ᅭᄒ ᆫᅧ ᅡ ᆫ ᄇᄉ ᅮᄅ ᅩᄉ ᆫᄌ ᅥ ᆼᄃ ᅥ ᅬᄌ ᅵᄋ ᆭᄋ ᅡ ᆻᄃ ᅡ ᅡ. Table 4.3 The variable importance of RF, GBM and XGboost variable sunlight duration prec cloud lat SO2 NO2 temp max O3 lon CO pressure mean PM10 rh mean PM25 spongy max temp min temp mean rh min spongy min cloud b spongy mean prec hr dew. RF 100 9.17 4.12 3.74 1.37 1.36 1.34 1.26 1.21 1.1 1.02 0.69 0.66 0.56 0.55 0.54 0.44 0.42 0.35 0.27 0.2 0.15 0.05 0.04. GBM 100.0 11.6 1.8 1.3 3.4 1.6 1.9 2.6 1.5 3.3 1.4 1.0 1.4 1.1 1.2 0.9 0.9 0.8 0.8 0.8 0.5 0.6 0.6 0.6. XGboost 100 12.11 7.48 15.73 3.09 0.95 1.21 2.99 1.43 3.19 0.74 0.28 0.49 1.2 1.29 2.42 0.86 0.86 2.08 0.99 2.75 0.98 0.80 0.34.
(7) Insolation prediction using air pollutants and meteorological variables. 1003. 5. 결론 ᅵᄋ ᄋ ᆫᄀ ᅧ ᅮᄂ ᆫᄀ ᅳ ᅪᄎ ᅢᄅ ᅲᄑ ᆷᄌ ᅮ ᆯᅧ ᅵ ᆯ 거 ᆼ ᄌᄋ ᅴᄀ ᅡᄌ ᆼᄒ ᅡ ᆨᄉ ᅢ ᆷᄌ ᅵ ᆨᄋ ᅥ ᆫᄋ ᅵ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᅦᄃ ᅢᄒ ᅡᄋ ᅧᄀ ᅵᄀ ᅨᄒ ᆨᄉ ᅡ ᆸᄋ ᅳ ᅴᅡ ᆼ ᄇᄇ ᆸᄋ ᅥ ᅳᄅ ᅩᄋ ᆯᄃ ᅵ ᆫᄋ ᅡ ᅱᄋ ᅨᄎ ᆨᄒ ᅳ ᅡ ᄋᄃ ᆻ ᅧ ᅡ. 2015ᄂ ᆫ 1ᄋ ᅧ ᆯ 1ᄋ ᅯ ᆯᄇ ᅵ ᅮᄐ ᅥ 2019ᄂ ᆫ 12ᄋ ᅧ ᆯ 31ᄋ ᅯ ᆯᄁ ᅵ ᅡᄌ ᅵᄋ ᅴᄀ ᅵᄉ ᆼᄌ ᅡ ᅡᄅ ᅭ, ᄃ ᅢᄀ ᅵᄋ ᅩᄋ ᆷᄆ ᅧ ᆯᄌ ᅮ ᆯᄌ ᅵ ᅡᄅ ᅭ, ᄋ ᆫᄅ ᅮ ᆼᄌ ᅣ ᅡᄅ ᅭᄅ ᆯᄉ ᅳ ᅮ ᆸᄒ ᅵ ᄌ ᅡᄋ ᅧ RF, GBM, XGboostᄅ ᆯᄌ ᅳ ᆨᄋ ᅥ ᆼᄒ ᅭ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄒ ᆫᄅ ᅮ ᆫᄌ ᅧ ᅡᄅ ᅭᅪ ᄋᄀ ᆷᄌ ᅥ ᆼᄌ ᅳ ᅡᄅ ᅭᄂ ᆫ 8:2ᄅ ᅳ ᅩᄀ ᅮᄇ ᆫᄒ ᅮ ᅡᄋ ᆻᄀ ᅧ ᅩᄒ ᆫᄅ ᅮ ᆫᄌ ᅧ ᅡᄅ ᅭᄋ ᅦᄃ ᅢ ᅢ 5ᄀ ᄒ ᆸ ᄀ ᅧ ᅭᄎ ᅡᄀ ᆷᄌ ᅥ ᆼᄋ ᅳ ᅳᄅ ᅩ ᄎ ᅬᄌ ᆨ ᄎ ᅥ ᅩᄆ ᅩᄉ ᅮᄅ ᆯ ᄉ ᅳ ᆫᄌ ᅥ ᆼᄒ ᅥ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄇ ᆫᄉ ᅮ ᆨᅧ ᅥ ᆯ ᄀᄀ ᅪ RFᄋ ᆯ ᄌ ᅳ ᆨᄋ ᅥ ᆼᄒ ᅭ ᆫ ᄋ ᅡ ᆯ ᄃ ᅵ ᆫᄋ ᅡ ᅱ ᄋ ᆯᄉ ᅵ ᅡᄅ ᆼ ᄋ ᅣ ᅨᄎ ᆨᄆ ᅳ ᅩᄒ ᆼᄋ ᅧ ᆫ ᅳ MAE=0.532, RMSE=0.718, R2 =0.979ᄅ ᅩᄀ ᅡᄌ ᆼᄋ ᅡ ᅮᄉ ᅮᄒ ᅡᄋ ᆻᄃ ᅧ ᅡ. ᄋ ᆯᄃ ᅵ ᆫᄋ ᅡ ᅱᄋ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᅨᄎ ᆨᄋ ᅳ ᅴᄌ ᆼᄋ ᅮ ᅭᄋ ᅭᄋ ᆫᄋ ᅵ ᆫᄋ ᅳ ᆯᄌ ᅵ ᅩ ᅵᄀ ᄉ ᆫᅵ ᅡ ᄋᄀ ᅩ, ᄋ ᅵᅬ ᄋᄋ ᅦᄀ ᅡᄌ ᅩᄉ ᅵᄀ ᆫ, ᄀ ᅡ ᆼᄉ ᅡ ᅮᄅ ᆼ, ᄋ ᅣ ᆫᄅ ᅮ ᆼ, ᄋ ᅣ ᅱᄃ ᅩᄃ ᆼᄋ ᅳ ᅴᄉ ᆫᄋ ᅮ ᅳᄅ ᅩᄌ ᆼᄋ ᅮ ᅭᄋ ᅭᄋ ᆫᄋ ᅵ ᅳᄅ ᅩᄉ ᆫᅥ ᅥ ᆼ ᄌᄃ ᅬᄋ ᆻᄃ ᅥ ᅡ. ᆫᄀ ᅧ ᄋ ᅮᄋ ᅴᄒ ᆫᄀ ᅡ ᅨᄌ ᆷᄆ ᅥ ᆾᄒ ᅵ ᆼᄒ ᅣ ᅮᄋ ᆫᄀ ᅧ ᅮᄂ ᅢᄋ ᆼᄋ ᅭ ᆫᄃ ᅳ ᅡᄋ ᆷᄀ ᅳ ᅪᄀ ᇀᄃ ᅡ ᅡ. ᄎ ᆺᄍ ᅥ ᅢ, ᄒ ᆫᄇ ᅡ ᆫᄃ ᅡ ᅩᄋ ᅴᄌ ᅵᄋ ᆨᄌ ᅧ ᆨᄐ ᅥ ᆨᄉ ᅳ ᆼᄋ ᅥ ᆯᄇ ᅳ ᆫᄋ ᅡ ᆼᄒ ᅧ ᆫᄋ ᅡ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᅨᄎ ᆨᄆ ᅳ ᅩ ᆼᄋ ᅧ ᄒ ᆫᅮ ᅧ ᄀᄀ ᅡᄑ ᆯᄋ ᅵ ᅭᄒ ᅡᄃ ᅡ. ᄒ ᆫᄇ ᅡ ᆫᄃ ᅡ ᅩᄋ ᅴᄋ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᅴᄀ ᆼᄀ ᅩ ᆫᄌ ᅡ ᆨᄇ ᅥ ᆫᄑ ᅮ ᅩᄅ ᆯᄉ ᅳ ᆯᄑ ᅡ ᅧᄇ ᅩᄆ ᆫᄌ ᅧ ᆼᄇ ᅮ ᅮᄌ ᅵᄋ ᆨᄋ ᅧ ᅵᄃ ᅡᄅ ᆫᄌ ᅳ ᅵᄋ ᆨᄋ ᅧ ᅦᄇ ᅵᄒ ᅢᄋ ᆯᄉ ᅵ ᅡᄅ ᆼ ᅣ ᅵ 6% ᄌ ᄋ ᆼᄃ ᅥ ᅩᄂ ᇁᄃ ᅩ ᅡ. ᄀ ᆼᄀ ᅩ ᆫᄀ ᅡ ᆫᄌ ᅮ ᆸᄇ ᅵ ᆫᄉ ᅮ ᆨᄋ ᅥ ᆯᄐ ᅳ ᆼᄒ ᅩ ᅢ 42ᄀ ᅢᄉ ᅩᄋ ᅴᄋ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᆯᅮ ᅳ ᆫ ᄀᄌ ᆸᄒ ᅵ ᅪᅡ ᆫ ᄒᄒ ᅮᄀ ᆫᄌ ᅮ ᆸᄇ ᅵ ᆯᄀ ᅧ ᆨᄌ ᅮ ᅵᄌ ᆨᄋ ᅥ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᅨᄎ ᆨᄆ ᅳ ᅩ ᆼᄋ ᅧ ᄒ ᆫᄀ ᅧ ᅮᄀ ᅡᄑ ᆯᄋ ᅵ ᅭᄒ ᅡᄃ ᅡ. ᄃ ᆯᄍ ᅮ ᅢ, ᄉ ᅵᄀ ᆫᄒ ᅡ ᅢᄉ ᆼᄃ ᅡ ᅩᄅ ᆯᄋ ᅳ ᆯᄃ ᅵ ᆫᄋ ᅡ ᅱᄋ ᅦᄉ ᅥᄉ ᅵᄀ ᆫᄃ ᅡ ᆫᄋ ᅡ ᅱᄅ ᅩᄒ ᆼᅡ ᅣ ᆼ ᄉᄒ ᅢᄋ ᅣᄒ ᆫᄃ ᅡ ᅡ. Fig 3.3ᄋ ᅦᄉ ᅥᄋ ᆯᄉ ᅡ ᅮ ᆻᄃ ᅵ ᄋ ᆺᅵ ᅳ ᄋᄀ ᅨᄌ ᆯᄀ ᅥ ᅪᄉ ᅵᄀ ᆫᄋ ᅡ ᅦᄄ ᅡᄅ ᅡᄀ ᆫᄎ ᅪ ᆨᄃ ᅳ ᆫᄋ ᅬ ᆯᄉ ᅵ ᅡᄅ ᆼᄋ ᅣ ᅴᄂ ᆼᄃ ᅩ ᅩᄂ ᆫᄃ ᅳ ᅡᄅ ᅳᄃ ᅡ. ᄀ ᅮᄅ ᆷᄋ ᅳ ᅴᄋ ᆼᄀ ᅣ ᅪᄆ ᅵᄉ ᅦᄆ ᆫᄌ ᅥ ᅵᄂ ᆫᄉ ᅳ ᅵᄀ ᆫᄒ ᅡ ᅢᄉ ᆼᄃ ᅡ ᅩᄀ ᅡᄂ ᇁ ᅩ ᆯᄄ ᅳ ᄋ ᅢᄋ ᅲᄋ ᅴᄆ ᅵᄒ ᆫᅧ ᅡ ᆫ ᄇᄉ ᅮᄀ ᅡᄃ ᆯᄉ ᅬ ᅮᄋ ᆻᄋ ᅵ ᅳᄆ ᅳᄅ ᅩᄉ ᅵᄀ ᆫᄒ ᅡ ᅢᄉ ᆼᄃ ᅡ ᅩᄅ ᆯᄒ ᅳ ᆼᅡ ᅣ ᆼ ᄉᄒ ᆫᄎ ᅡ ᅮᄀ ᅡᄋ ᆫᄀ ᅧ ᅮᄀ ᅡᄑ ᆯᄋ ᅵ ᅭᄒ ᅡᄃ ᅡ.. References Ahn, S. H., Zo, I. S., Jee, J. B., Kim, B. Y., Lee, D. G., & Lee, K. T. (2016). The estimation of monthly average solar radiation using sunshine duration and precipitation observation data in Gangneung region. Journal of the Korean earth science society, 37(1), 29-39. An, S., Lim, Y. (2020). Forecasting daily PM 10 concentration in Seoul Jong-no district by using various statistical techniques. Journal of the Korean Data And Information Science Society, 31(1), 187-198. Bae, S., Yu, J. (2018). Predicting the real estate price index using machine learning methods and time series analysis model Housing Studies, 26(1), 107-133. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. Chai, T., Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)??Arguments against avoiding RMSE in the literature. Geoscientific model development, 7(3), 1247-1250. Chen, T., Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Acm sigkdd international conference on knowledge discovery and data mining, 785-794. Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., & Cho, H. (2015). Xgboost: extreme gradient boosting. R package version 0.4-2 , 1(4). Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 29(5), 1189-1232. Jeong, H. (2020). Short-term forecasting for wind speed based on machine learning using weather observation data. Journal of the Korean Data And Information Science Society, 31(5), 823-837. Jung, Y., Cho, H., Kim, J., Kim, Y., Kim, Y. (2011). The effects of clouds on enhancing surface solar irradiance. Atmosphere, 21(2), 131-142. Kim, G. Kim, Y. (2017). A survey on oil spill and weather forecast using machine learning based on neural networks and statistical methods. Journal of the Korea Convergence Society, 8(10), 1-8. Kim, J. (2019). A solar power prediction scheme based on machine learning algorithm from weather forecasts. The Journal of Korean Institute of Information Technology, 17(9), 83-89. Kim, M., Hong S., Yoon, S. (2018). Comparison of peach meridian price and trading volume prediction model using machine learning. Journal of The Korean Data Analysis Society, 20(6), 2933-2940. Kim, M., Jung, S., Kim, J., Lee, H., Kim, S. (2019). A study on artificial neural network-based solar radiation forecasting for efficient solar photovoltaic system. Journal of Korean Institute of Intelligent Systems, 29(6), 501-506. Kim, M., Jung, S., Kim, J., Lee, H., Kim, S. (2021). A study on solar radiation forecasting based on long short-term memory considering hourly weather changes. Journal of Korean Institute of Intelligent Systems,31(1), 88-94. Kim, S. (2017). A study on solar irradiance forecasting with weather variables. The Korean Journal of Applied Statistics,, 30(6), 1005-1013..
(8) 1004. Yeongeun Hwang · Dayoung Kang · Myunghwan Na · Sanghoo Yoon. Kim, Y. E., Lee, K. E., Kim, G. (2020). Forecast of drought index using decision tree based methods. Journal of the Korean Data And Information Science Society, 31(2), 273-288. Kohavi. and Ron. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence, 14(12), 1137-43. Korea Rural Economic Institute (2004). An estimation of yield functions of Korean fruit-vegetables. Policy research report, P73. Retrieved from http://repository.krei.re.kr/handle/2018.oak/14720. Lee, G., Lee, G., Kang, S. (2017). A case study for analyzing the optimal location for a solar power plant via AHP analysis with fine dust and weather information. Journal of the Korea Safety Management & Science, 19(4), 157-167. Lee, S., Choi, H., Lee, D., Kim, J. (2011). Economic Evaluation Method for Photovoltaic System Development using Insolation Data Analysis. Journal of the Korean Institute of Illuminating and Electrical Installation Engineers, 25(10), 38-46. Lee, Y., Bae, J.., Park, J. (2017). A study on prediction techniques through machine learning of real-time solar radiation in Jeju. Journal of Environmental Science International, 26(4), 521-527. Liaw, A., Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22. Molinaro, A. M., Simon, R., Pfeiffer, R. M. (2005). Prediction error estimation: a comparison of resampling methods. Bioinformatics, 21(15), 3301-3307. Oh, J., Ham, D., Lee, Y., Kim, G. (2019). Short-term load forecasting using XGBoost and the analysis of hyperparameters. Trans. Korean. Inst. Elect. Eng., 68(9), 1073-1078. Park, S., Soung D., Byun Y. (2020). A hybrid collaborative filtering based on online shopping patterns using XGBoost and Word2Vec. Journal of Korean Institute of Information Technology, 18(9), 1-8. Ridgeway, G., Ridgeway, M. G. (2004). The gbm package. R Foundation for Statistical Computing, Vienna, Austria, 5(3). Suh, Y. M., Son, H. G., Kim, S. (2018). Solar radiation forecasting by time series models. The Korean Journal of Applied Statistics, 31(6), 785-799. Won, J. M., Doe, G. Y., Heo, N. R. (2011). Predict solar radiation according to weather report. Journal of Navigation and Port Research, 35(5), 387-392. Yoo Jin Eun. (2015). Random forests, an alternative data mining technique to decision tree. Journal of Educational Evaluation, 28(2), 427-448. Yun, J. I. (2009). A simple method using a topography correction coefficient for estimating daily distribution of solar irradiance in complex terrain. Korean journal of agricultural and forest meteorology, 11(1), 13-18..
(9) Journal of the Korean Data & Information Science Society 2021, 32(5), 997–1005. http://dx.doi.org/10.7465/jkdi.2021.32.5.997 ᆫᄀ ᅡ ᄒ ᆨᄃ ᅮ ᅦᄋ ᅵᄐ ᅥᄌ ᆼᄇ ᅥ ᅩᅪ ᄀᄒ ᆨᄒ ᅡ ᅬᄌ ᅵ. Insolation prediction using air pollutants and meteorological variables. †. Yeongeun Hwang1 · Dayoung Kang2 · Myunghwan Na3 · Sanghoo Yoon4 12. Department of Statistics, Daegu University Department of Statistics, Chonnam National University 4 Division of Mathematics and Big Data Science, Daegu University 3. Received 22 June 2021, revised 22 July 2021, accepted 28 July 2021. Abstract The quality of fruits and vegetables is affected by exposure to insolation at each stage of growth. A machine-learning study using a time series model reflecting the time series characteristics of insolation and meteorological variables affecting insolation was performed. This study presents a model for predicting insolation in a tree-based ensemble that considers both atmospheric pollutant concentrations and meteorological variables that can affect surface insolation. The research data were collected from the Korea Meteorological Administrator and Air Korea, and the research period was between 2015 and 2019. The daily insolation was predicted through machine learning, Random Forest (RF), gradient boosting model (GBM), and XGboost. 5-fold cross-validation was used for model validation, and prediction performance was compared with mean absolute value error, root mean square error, and coefficient of determination. GBM was the best predictive performance among models through 5-fold cross-validation, but with overfitting. Therefore, as a result of applying the optimized parameters, the RF prediction was the best. Both sunshine time and duration were very important variables. However, the amount of clouds and fine dust concentration are not important variables in predicting. Keywords: Extreme gradient boosting machine, gradient boosting, insolation, randomforest.. †. This work was supported by the research program of Rural Development Administration(Project No. PJ0153372021). 1 Master’s course, Department of statistics, Daegu University, Gyeongbuk 38453, Korea. 2 Master’s course, Department of statistics, Daegu University, Gyeongbuk 38453, Korea. 3 Professor, Department of Statistics, Chonnam National University, Gwangju 61186, Korea. 4 Corresponding author: Assistant professor, Division of Mathematics and Big Data Science, Daegu University, Gyeongbuk 38453, Korea. E-mail: [email protected].
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