Nowadays, artificial intelligence model approaches such as machine and deep learning have been widely used to predict variations of water quality in various freshwater bodies. In particular, many researchers have tried to predict the occurrence of cyanobacterial blooms in inland water, which pose a threat to human health and aquatic ecosystems. Therefore, the objective of this study were to: 1) review studies on the application of machine learning models for predicting the occurrence of cyanobacterial blooms and its metabolites and 2) prospect for future study on the prediction of cyanobacteria by machine learning models including deep learning. In this study, a systematic literature search and review were conducted using SCOPUS, which is Elsevier’s abstract and citation database. The key results showed that deep learning models were usually used to predict cyanobacterial cells, while machine learning models focused on predicting cyanobacterial metabolites such as concentrations of microcystin, geosmin, and 2-methylisoborneol (2-MIB) in reservoirs. There was a distinct difference in the use of input variables to predict cyanobacterial cells and metabolites. The application of deep learning models through the construction of big data may be encouraged to build accurate models to predict cyanobacterial metabolites.

The prediction of algal bloom is an important field of study in algal bloom management, and chlorophyll-a concentration(Chl-a) is commonly used to represent the status of algal bloom. In, recent years advanced machine learning algorithms are increasingly used for the prediction of algal bloom. In this study, XGBoost(XGB), an ensemble machine learning algorithm, was used to develop a model to predict Chl-a in a reservoir. The daily observation of water quality data and climate data was used for the training and testing of the model. In the first step of the study, the input variables were clustered into two groups(low and high value groups) based on the observed value of water temperature(TEMP), total organic carbon concentration(TOC), total nitrogen concentration(TN) and total phosphorus concentration(TP). For each of the four water quality items, two XGB models were developed using only the data in each clustered group(Model 1). The results were compared to the prediction of an XGB model developed by using the entire data before clustering(Model 2). The model performance was evaluated using three indices including root mean squared error-observation standard deviation ratio(RSR). The model performance was improved using Model 1 for TEMP, TN, TP as the RSR of each model was 0.503, 0.477 and 0.493, respectively, while the RSR of Model 2 was 0.521. On the other hand, Model 2 shows better performance than Model 1 for TOC, where the RSR was 0.532. Explainable artificial intelligence(XAI) is an ongoing field of research in machine learning study. Shapley value analysis, a novel XAI algorithm, was also used for the quantitative interpretation of the XGB model performance developed in this study.

본 연구는 인공신경망의 성능을 향상시키기 위한 여러 가지 방법들 중의 하나인 입력변수 선정기법에 관한 연구로서, 일반적으로 널리 사용되고 있는 상관계수를 이용한 입력변수 선정기법 외에 상호정보량을 활용한 방법을 적용하여 인공신경망의 성능을 향상시키고자 하였다. 대상자료는 기상청에서 제공하는 RDAPS자료의 152개 출력값으로 지상강우량의 예측값인 APCP를 포함하고 있으며, 강우관측값간의 상호정보량을 구해 가장 영향력이 큰 변수를 입력변수로 사용하였다.

본 논문에서는, 1995년도, 2000년도, 2004년도, 국내 26개 항만들을 대상으로 2개의 투입변수(접안능력, 하역능력)와 3개의 산출변수(수출화물처리량, 수입화물처리량, 입출항척수)가 있는 경우에 구성될 수 있는 21개의 DEA모형을 제시하고 효율성을 측정하였다. 또한 그러한 효율성 측정결과를 이용하여 주성분분석을 시행하여 핵심투입변수와 산출변수를 추출하였다. 실증분석의 핵심적인 결과를 살펴보면, 핵심투입 변수는 하역능력, 핵심산출변수는 입출항척수로 나타났다. 정책적인 함의는 항만정책당국이 개별항만들의 핵심투입변수와 산출변수가 어떻게 변화해 왔는지를 검토하여 차후 항만투자와 개발 시에 반드시 고려하고 반영해야만 한다.