<KSFEA 2018 춘계학회 초록 양식> *반드시 아래 표에 체크(√)해주세요*
발표 형태		발표 분야
구두	포스터	대기	수질	토양/퇴적물/폐기물	분석	위해성/기타
	√		√

통계모형을 이용한 한강수계 주요 수질항목의 단기 수질예측

(Forecasting short-term water quality of Han River by using ARIMA model)

이진효 · 조수석 · 천정완 · 양진영 · 김진아 · 길혜경 · 이목영 · 정권 · 구자용*

서울특별시보건환경연구원, 서울시립대학교 환경공학부*

*Corresponding author: jykoo@uos.ac.kr

Abstract

The measurement values produced by automatic water quality monitoring networks (AWQMNs) are a kind of time series data extracted at regular time intervals. Therefore, if a time series model is mathematically expressed to appropriately simulate the process of producing the measured values, it will be possible to understand and control the prediction of the water quality in the future. In this study, for the construction of the time series model, the predictive power of the time series model was verified using the monthly average measurement of Han River S water quality automatic measuring network from January 2007 to March 2018. DO, TN, and TP were selected as the water quality items. An analytical method is the autoregressive integrated moving average (ARIMA) model with an increasing number of applications using R 3.4.3. As a result, in the case of Han River S automatic water quality monitoring network, each model of DO, TN and TP was ARIMA(1,0,0)(2,0,0)12, ARIMA(1,0,0)(2,0,0)12 and ARIMA(1,1,1)(2,0,1)12 respectively, which predicted monthly measurements from April 2018 to March 2019. Also in order to verify the suitability of each ARIMA model, the predicted values for 2017 were estimated using the data from 2007 to 2016, and compared with the actual measured values. As a result, each MAPE (%) of 3 items was DO 11.16942% TN 19.69388%, and TP 21.88649%. All three items showed relatively reasonable prediction power. These models were able to predict three items from April 2018 to March 2019.

Key words: AWQMNs, ARIMA, Time Series Model, R 3.4.3