Algal bloom is an ongoing issue in the management of freshwater systems for drinking water supply, and the chlorophyll-a concentration is commonly used to represent the status of algal bloom. Thus, the prediction of chlorophyll-a concentration is essential for the proper management of water quality. However, the chlorophyll-a concentration is affected by various water quality and environmental factors, so the prediction of its concentration is not an easy task. In recent years, many advanced machine learning algorithms have increasingly been used for the development of surrogate models to prediction the chlorophyll-a concentration in freshwater systems such as rivers or reservoirs. This study used a light gradient boosting machine(LightGBM), a gradient boosting decision tree algorithm, to develop an ensemble machine learning model to predict chlorophyll-a concentration. The field water quality data observed at Daecheong Lake, obtained from the real-time water information system in Korea, were used for the development of the model. The data include temperature, pH, electric conductivity, dissolved oxygen, total organic carbon, total nitrogen, total phosphorus, and chlorophyll-a. First, a LightGBM model was developed to predict the chlorophyll-a concentration by using the other seven items as independent input variables. Second, the time-lagged values of all the input variables were added as input variables to understand the effect of time lag of input variables on model performance. The time lag (i) ranges from 1 to 50 days. The model performance was evaluated using three indices, root mean squared error-observation standard deviation ration (RSR), Nash-Sutcliffe coefficient of efficiency (NSE) and mean absolute error (MAE). The model showed the best performance by adding a dataset with a one-day time lag (i=1) where RSR, NSE, and MAE were 0.359, 0.871 and 1.510, respectively. The improvement of model performance was observed when a dataset with a time lag up of about 15 days (i=15) was added.

오늘날의 전반적인 산업분야에서는 정보기술을 바탕으로 운영되고, 이에 대한 투자가 증가되고 있다. 물류산업에서도 물류정보망의 중요성이 커지고, 효율적으로 운영하기 위한 정보화 투자가 증가되고 있다. 효율성에 관한 기존연구에서는 설비와 같은 고정자산만을 고려한 효율성 분석으로 이루어져, 정보화 수준이 효율성에 미치는 영향에 관한 연구는 미흡하다. 본 연구는 컨테이너 터미널 효율적 운영을 위하여 정보화 수준을 고려한 효율성 분석의 중요성을 제시하고, DEA과 Bootstrap를 이용하여 정보화 수준과 관련된 상대적 효율성을 측정하였다.

In order to how well predict ISCST3(Industrial Source Complex Short Term version 3) model dispersion of air pollutant at point source, sensitivity was analysed necessary parameters change. ISCST3 model is Gaussian plume model. Model calculation was performed with change of the wind speed, atmospheric stability and mixing height while the wind direction and ambient temperature are fixed. Fixed factors are wind direction as the south wind(180˚) and temperature as 298 K(25℃). Model's sensitivity is analyzed as wind speed, atmospheric stability and mixing height change. Data of stack are input by inner diameter of 2m, stack height of 30m, emission temperature of 40℃, outlet velocity of 10m/s. On the whole, main factor which affects in atmospheric dispersion is wind speed and atmospheric stability at ISCST3 model. However it is effect of atmospheric stability rather than effect of distance downwind. Factor that exert big influence in determining point of maximum concentration is wind speed. Meanwhile, influence of mixing height is a little or almost not.