[논문]다변량 선형회귀분석을 이용한 증발접시계수 산정방법 적용성 검토

임창수

doi:10.3741/jkwra.2022.55.3.229

다변량 선형회귀분석을 이용한 증발접시계수 산정방법 적용성 검토
Evaluation of applicability of pan coefficient estimation method by multiple linear regression analysis 원문보기

Journal of Korea Water Resources Association = 한국수자원학회논문집, v.55 no.3, 2022년, pp.229 - 243

임창수 (경기대학교 건설시스템공학 전공)

초록
AI-Helper

우리나라 11개 기상관측지역의 월별 기상자료가 증발접시계수에 미치는 영향을 분석하고, 증발접시계수 산정을 위한 4가지 형태의 다변량 선형회귀모형의 적용성을 검토하였다. 개발된 증발접시계수 산정모형의 적용성을 평가하기 위해서 기존에 다른 연구자들에 의해서 제안된 6가지의 모형과 비교 평가하였다. 우리나라 11개 기상관측지역에서 증발접시계수는 1, 2, 3, 7, 11, 12월은 기온에 가장 큰 영향을 받고, 다른 월들은 일사량에 가장 큰 영향을 받는 것으로 나타났다. 전반적으로 모든 월에서 풍속과 상대습도는 기온이나 일사량과 비교해서 증발접시계수에 큰 영향을 미치지 않는 것으로 나타났다. 모든 지역과 월에서 각 지역별로 5개의 독립변수(풍속, 상대습도, 기온, 일조시간과 가조시간의 비, 일사량)를 적용하여 유도된 모형이 가장 양호한 증발량 산정 결과를 보였다. 모형 검증결과에 의하면 다변량 선형회귀분석을 적용하여 증발접시계수를 산정하는 경우 일부 지역과 월에서 제한적으로 적용할 수 있을 것으로 판단된다.

Abstract ▼ AI-Helper

The effects of monthly meteorological data measured at 11 stations in South Korea on pan coefficient were analyzed to develop the four types of multiple linear regression models for estimating pan coefficients. To evaluate the applicability of developed models, the models were compared with six previous models. Pan coefficients were most affected by air temperature for January, February, March, July, November and December, and by solar radiation for other months. On the whole, for 12 months of the year, the effects of wind speed and relative humidity on pan coefficient were less significant, compared with those of air temperature and solar radiation. For all meteorological stations and months, the model developed by applying 5 independent variables (wind speed, relative humidity, air temperature, ratio of sunshine duration and daylight duration, and solar radiation) for each station was the most effective for evaporation estimation. The model validation results indicate that the multiple linear regression models can be applied to some particular stations and months.

주제어

표/그림 (15)

그림 Fig. 1. The location map of 11 study stations
표 Table 1. Models for estimating pan coefficients
표 Table 2. Statistical significance of meteorological data on pan coefficient
표 Table 3. Monthly NSE coefficients at Seoul station
표 Table 4. Monthly NSE coefficients at Incheon station
표 Table 5. Monthly NSE coefficients at Suwon station
표 Table 6. Monthly NSE coefficients at Seosan station
표 Table 7. Monthly NSE coefficients at Cheongju station
표 Table 8. Monthly NSE coefficients at Daejeon station
표 Table 9. Monthly NSE coefficients at Pohang station
표 Table 10. Monthly NSE coefficients at Jeonju station
표 Table 11. Monthly NSE coefficients at Busan station
표 Table 12. Monthly NSE coefficients at Mokpo station
그림 Fig. 2. A one-to-one relationship between measured and estimated evaporation (applying all stations)
표 Table 13. Monthly NSE coefficients at Jeju station

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