상세정보
학술대회
Enhancing Prediction of Chlorophyll-a Concentration with Feature Extraction using Higher-Order Partial Least Squares
- 발행일
- 202010
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1666-1668
- 협약과제
- 20HB1700, 직독식 수질복합센서 및 초분광영상 기반 시공간 복합 인공지능 녹조 예측 기술, 권용환
- 초록
- Harmful algal blooms can cause significant negative impacts on the health of humans and other organisms. It is useful to predict chlorophyll-a concentration accurately for the forecast of algal blooms. While convolutional machine learning models are often used for such prediction, they may not fully consider the relationships among input features. We propose an approach to enhance the prediction by extracting latent features using higher-order partial least squares (HOPLS). The experimental results show that the feature extraction using HOPLS can significantly improve the prediction accuracy, especially in critical cases.
- KSP 제안 키워드
- Feature extractioN, Harmful Algal Blooms, Higher-order Partial Least Squares, Input features, Latent feature, Least Squares(LS), Negative impact, Partial least-squares(PLS), Prediction accuracy, chlorophyll-a concentration, machine learning models