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학술대회 Modelling Chlorophyll-a Concentration using Deep Neural Networks considering Extreme Data Imbalance and Skewness
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저자
최장호, 김지용, 원종호, 민옥기
발행일
201902
출처
International Conference on Advanced Communications Technology (ICACT) 2019, pp.631-634
DOI
https://dx.doi.org/10.23919/ICACT.2019.8702027
협약과제
18HB2700, 직독식 수질복합센서 및 초분광영상 기반 시공간 복합 인공지능 녹조 예측 기술, 권용환
초록
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.
키워드
Algal Bloom, Data Imbalance, Data Skewness, Neural Network, Regression, Sensor-data regression, Water Quality
KSP 제안 키워드
Accurate prediction, Algal blooms, Convolution neural network(CNN), Data imbalance, Data regression, Data skewness, Deep neural network(DNN), Experiment results, Machine Learning Algorithms, Prediction accuracy, Water quality