지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.2
- 수록면
- 109 - 122 (14page)
이용수
초록· 키워드
오류제보하기
Harmful algal blooms (HABs) have been frequently occurred with releasing toxic substances, which typically lead to water quality degradation and health problems for humans and aquatic animals. Hence, accurate quantitative analysis and prediction of HABs should be implemented to detect, monitor, and manage severe algal blooms. However, the traditional monitoring required sufficient expense and labor while numerical models were restricted in terms of their ability to simulate the algae dynamic. To address the challenging issue, this study evaluates the applicability of deep learning to simulate chlorophyll-a (Chl-a) and phycocyanin (PC) with the internet of things (IoT) system. Our research adopted LSTM models for simulating Chl-a and PC. Among LSTM models, the attention LSTM model achieved superior performance by showing 0.84 and 2.35 (μg/L) of the correlation coefficient and root mean square error. Among preprocessing methods, the z-score method was selected as the optimal method to improve model performance. The attention mechanism highlighted the input data from July to October, indicating that this period was the most influential period to model output. Therefore, this study demonstrated that deep learning with IoT system has the potential to detect and quantify cyanobacteria, which can improve the eutrophication management schemes for freshwater reservoirs.
목차
ABSTRACT
1. Introduction
2. Materials and Methods
3. Result and Discussion
4. Conclusions
References
참고문헌 (97)
참고문헌 신청
Bae S-S, Pyo J, Parchepsky Y, et al. Identification and enumeration of cyanobacteria species using a deep neural network. Ecol. Indic. 2020;115:106395.
Luo Y, Yang K, Yu Z, et al. Dynamic monitoring and prediction of Dianchi Lake cyanobacteria outbreaks in the context of rapid urbanization. Environ. Sci. Pollut. Res. 2017;24(6):5335-5348.
Michalak AM. Study role of climate change in extreme threats to water quality. Nature News. 2016;535(7612):349.
Joung S-H, Oh H-M, Ko S-R, Ahn C-Y. Correlations between environmental factors and toxic and non-toxic Microcystis dynamics during bloom in Daechung Reservoir, Korea. Harmful Algae. 2011;10(2):188-193.
Cha Y, Park SS, Kim K, Byeon M, Stow CA. Probabilistic prediction of cyanobacteria abundance in a Korean reservoir using a Bayesian Poisson model. Water Resour. Res. 2014;50(3):2518-2532.
Luo Y, Yang K, Yu Z, et al. Dynamic monitoring and prediction of Dianchi Lake cyanobacteria outbreaks in the context of rapid urbanization. Environ. Sci. Pollut. Res. 2017;24(6):5335-5348.
Michalak AM. Study role of climate change in extreme threats to water quality. Nature News. 2016;535(7612):349.
Joung S-H, Oh H-M, Ko S-R, Ahn C-Y. Correlations between environmental factors and toxic and non-toxic Microcystis dynamics during bloom in Daechung Reservoir, Korea. Harmful Algae. 2011;10(2):188-193.
Cha Y, Park SS, Kim K, Byeon M, Stow CA. Probabilistic prediction of cyanobacteria abundance in a Korean reservoir using a Bayesian Poisson model. Water Resour. Res. 2014;50(3):2518-2532.
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