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
- 2024.4
- 수록면
- 70 - 80 (11page)
이용수
초록· 키워드
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The algal blooms caused by eutrophication is a major concern in water and aquatic resource management, and various attempts have been made to accurately predict it. Algal blooms and the factors influencing them exhibit high spatial variability depending on the characteristics of the water body and water flow. However, traditional machine learning and deep learning methods have limitations to account for the spatial interactions of various influencing factors across multiple monitoring sites. In addition, attempts to predict multiple sites simultaneously using a single model are limited. In this study, we proposed a model that considers spatial interactions and performs multisite predictions based on a graph attention network (GAT). The GAT–DNN, which combines a deep neural network (DNN) after GAT layer, was applied to forecast chlorophyll-a levels at multiple sites. The proposed model accurately captured the high variability and peak chlorophyll-a levels. Moreover, the GAT–DNN consistently outperformed two baseline DNNs in both cases. Additionally, we examined the optimal forecast horizon by comparing the performance of the model across various forecast horizons. Therefore, the proposed model can be applied to a wide range of prediction models to capture spatial interactions and obtain the benefits of performance outcomes for each site.
목차
ABSTRACT
1. Introduction
2. Methods
3. Results and Discussion
4. Conclusions
References
참고문헌 (57)
참고문헌 신청
Ly QV, Nguyen XC, Lê NC et al. Application of Machine Learning for eutrophication analysis and algal bloom prediction in an urban river: A 10-year study of the Han River, South Korea. Sci. Total. Environ. 2021;797:149040. https://doi.org/10.1016/J.SCITOTENV.2021.149040.
Massey IY, Osman A, M, Yang F. An overview on cyanobacterial blooms and toxins production: their occurrence and influencing factors. Toxin. Rev. 2020;41:326-346. https://doi.org/10.1080/15569543.2020.1843060.
Hamilton DP, Wood SA, Dietrich DR, et al. Costs of Harmful Blooms of Freshwater Cyanobacteria. In: Cyanobacteria: An Economic Perspective John Wiley & Sons, Ltd; 2013; p. 245–256. https://doi.org/10.1002/9781118402238.CH15.
Davidson K, Anderson DM, Mateus M et al. Forecasting the risk of harmful algal blooms. Harmful. Algae. 2016;53:1-7. https://doi.org/10.1016/j.hal.2015.11.005.
Cho H, Choi U-J, Park H. Deep learning application to time-series prediction of daily chlorophyll-a concentration. WIT. Trans. Ecol. Environ. 2018;215:157-163. https://doi.org/10.2495/EID180141.
Massey IY, Osman A, M, Yang F. An overview on cyanobacterial blooms and toxins production: their occurrence and influencing factors. Toxin. Rev. 2020;41:326-346. https://doi.org/10.1080/15569543.2020.1843060.
Hamilton DP, Wood SA, Dietrich DR, et al. Costs of Harmful Blooms of Freshwater Cyanobacteria. In: Cyanobacteria: An Economic Perspective John Wiley & Sons, Ltd; 2013; p. 245–256. https://doi.org/10.1002/9781118402238.CH15.
Davidson K, Anderson DM, Mateus M et al. Forecasting the risk of harmful algal blooms. Harmful. Algae. 2016;53:1-7. https://doi.org/10.1016/j.hal.2015.11.005.
Cho H, Choi U-J, Park H. Deep learning application to time-series prediction of daily chlorophyll-a concentration. WIT. Trans. Ecol. Environ. 2018;215:157-163. https://doi.org/10.2495/EID180141.
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