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
- 2019.9
- 수록면
- 404 - 411 (8page)
이용수
초록· 키워드
오류제보하기
In this study, we designed a data-driven model to predict chlorophyll-a using M5P model tree and extreme learning machine (ELM). The Juksan weir in the Youngsan River has high chlorophyll-a, which is the primary indicator of algal bloom every year. Short-term algal bloom prediction is important for environmental management and ecological assessment. Two models were developed and evaluated for short-term algal bloom prediction. M5P is a classification and regression-analysis-based method, and ELM is a feed-forward neural network with fast learning using the least square estimate for regression. The dataset used in this study includes water temperature, rainfall, solar radiation, total nitrogen, total phosphorus, N/P ratio, and chlorophyll-a, which were collected on a daily basis from January 2013 to December 2016. The M5P model showed that the prediction model after one day had the highest performance power and dropped off rapidly starting with predictions after three days. Comparing the performance power of the ELM model with the M5P model, it was found that the performance power of the 1-7 d chlorophyll-a prediction model was higher. Moreover, in a period of rapidly increasing algal blooms, the ELM model showed higher accuracy than the M5P model.
목차
ABSTRACT
1. Introduction
2. Materials and Methods
3. Results
4. Conclusions
References
참고문헌 (30)
참고문헌 신청
Anderson DM, Cembella AD, Hallegraeff GM. Progress in understanding harmful algal blooms: Paradigm shifts and new tech-nologies for research, monitoring, and management. Annu. Rev. Mar. Sci. 2012;4:143-176.
Glasgow HB, Burkholder JM, Reed RE, Lewitus AJ, Kleinman JE. Real-time remote monitoring of water quality: A review of current applications, and advancements in sensor, telemetry, and computing technologies. J. Exp. Mar. Biol. Ecol. 2004;300: 409-448.
Conley DJ, Paerl HW, Howarth RW, et al. Controlling eutrophication: Nitrogen and phosphorus. Science 2009;323:1014-1015.
Paerl HW. Controlling cyanobacterial harmful blooms in freshwater ecosystems, microbial biotechnology. Microb. Biotechnol. 2017;10:1106-1110.
Zhang X, Recknagel F, Chen Q, Cao H, Li R. Spatially-explicit modelling and forecasting of cyanobacteria growth in Lake Taihu by evolutionary computation. Ecol. Modell. 2014;306:216-225.
Glasgow HB, Burkholder JM, Reed RE, Lewitus AJ, Kleinman JE. Real-time remote monitoring of water quality: A review of current applications, and advancements in sensor, telemetry, and computing technologies. J. Exp. Mar. Biol. Ecol. 2004;300: 409-448.
Conley DJ, Paerl HW, Howarth RW, et al. Controlling eutrophication: Nitrogen and phosphorus. Science 2009;323:1014-1015.
Paerl HW. Controlling cyanobacterial harmful blooms in freshwater ecosystems, microbial biotechnology. Microb. Biotechnol. 2017;10:1106-1110.
Zhang X, Recknagel F, Chen Q, Cao H, Li R. Spatially-explicit modelling and forecasting of cyanobacteria growth in Lake Taihu by evolutionary computation. Ecol. Modell. 2014;306:216-225.
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UCI(KEPA) : I410-ECN-0101-2019-539-000448607