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학술대회 Modelling Chlorophyll-a Concentration using Deep Neural Networks considering Extreme Data Imbalance and Skewness
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최장호, 김지용, 원종호, 민옥기
International Conference on Advanced Communications Technology (ICACT) 2019, pp.631-634
18HB2700, 직독식 수질복합센서 및 초분광영상 기반 시공간 복합 인공지능 녹조 예측 기술, 권용환
Algal bloom has been a serious problem, as some of algae such as cyanobacteria produce toxic wastes. Chlorophyll-a has been one of the primary indicator of algal bloom; however, it is difficult to model to forecast due to scarceness of the events. Since canonical machine learning algorithms assume balanced datasets, data imbalance of the Chlorophyll-a concentration must be visited for accurate prediction. In this paper, we present a convolutional neural network model to predict Chlorophyll-a concentration, handling its data imbalance and skewness. The experiment results show that proper data transformation and oversampling can improve prediction accuracy, especially in rare-event regions.
KSP 제안 키워드
Accurate prediction, Algal blooms, Convolution neural network(CNN), Data imbalance, Deep neural network(DNN), Experiment results, Machine Learning Algorithms, Prediction accuracy, chlorophyll-a concentration, data transformation, neural network model