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Conference Paper
Modelling Chlorophyll-a Concentration using Deep Neural Networks considering Extreme Data Imbalance and Skewness
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- Authors
- Jang-Ho Choi, Jiyong Kim, Jongho Won, Okgee Min
- Issue Date
- 2019-02
- Citation
- International Conference on Advanced Communications Technology (ICACT) 2019, pp.631-634
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- 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 Keywords
- 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