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학술대회 Exploiting Heterogeneous Monitoring Data for Spatiotemporal Algal Bloom Prediction
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저자
이태휘, 장미영, 최장호, 원종호, 김지용
발행일
202202
출처
International Conference on Artificial Intelligence in Information and Communication (ICAIIC) 2022, pp.443-445
DOI
https://dx.doi.org/10.1109/ICAIIC54071.2022.9722642
협약과제
21HB1300, 직독식 수질복합센서 및 초분광영상 기반 시공간 복합 인공지능 녹조 예측 기술, 권용환
초록
Harmful algal blooms need to be mitigated because they can cause significant negative effects to humans and other organisms. If such algal blooms can be predicted in advance by monitoring water quality, they can be suppressed at an early stage by making decisions to take actions. We describe our ongoing work on integrating the heterogeneous water quality monitoring data and on recovering the missing data using tensor completion techniques. We also discuss the challenges in carrying out this study.
KSP 제안 키워드
Bloom prediction, Harmful Algal Blooms, Missing data, Monitoring data, Negative effects, Tensor completion, Water quality monitoring