머신러닝를 이용한 낙동강 본류 구간 수문-기상인자 조류 예보체계 연구
A study on algal bloom forecast system based on hydro-meteorological factors in the mainstream of Nakdong river using machine learning
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초록
남조류의 대량 발생은 담수 생태계에서 수체 내 산소 고갈, 악취 및 독성물질 분비로 인하여 수생태계 및 정수공급체계에 악영향을 미친다. 이러한 녹조현상은 낙동강 보 건설 이후 조류의 수체 내 체류시간 증가와 더불어 기후변화로인한 지표면 기온 상승으로 인하여 강도와 빈도가 증가할 것으로 예상된다. 본 연구에서는 예상되는 녹조현상의 증가에 대응하기 위하여 조류경보 발령 이전에 선제적 대응을 위한 수문-기상인자 조류 예보체계를 제시하였다. 다연상관분석을 통하여 조류예측 단계에 따른 기온 및 유량의 선행 영향기간을 탐색하였다. 머신러닝 기법인 의사결정나무 분류를 통하여 선행 기간의 기온 및 유량에 따른 조류예측 단계 분류모델을 도출하였고, 분류모델 결과를 기반으로 수문-기상인자 조류 예보체계를 도출하였다. 제시한 수문-기상인자 조류 예보체계는 녹조현상 발생 이전의 선제적 대응을 위한 기초 연구로써 활용될 수 있을 것으로 판단된다.
Abstract
Blue-green algal bloom, or harmful algal bloom has a negative impact on the aquatic ecosystem and purified water supply system due to oxygen depletion in the water body, odor, and secretion of toxic substances in the freshwater ecosystem. This Blue-green algal bloom is expected to increase in intensity and frequency due to the increase in algae's residence time in the water body after the construction of the Nakdong River weir, as well as the increase in surface temperature due to climate change. In this study, in order to respond to the expected increase in green algae phenomenon, an algal bloom forecast system based on hydro-meteorological factors was presented for preemptive response before issuing a algal bloom warning. Through polyserial correlation analysis, the preceding influence periods of temperature and discharge according to the algal bloom forecast level were derived. Using the decision tree classification, a machine learning technique, Classification models for the algal bloom forecast levels based on temperature and discharge of the preceding period were derived. And a algal bloom forecast system based on hydro-meteorological factors was derived based on the results of the decision tree classification models. The proposed algae forecast system based on hydro-meteorological factors can be used as basic research for preemptive response before blue-green algal blooms.
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