期刊
HARMFUL ALGAE
卷 43, 期 -, 页码 58-65出版社
ELSEVIER
DOI: 10.1016/j.hal.2015.01.002
关键词
Algal bloom; ARIMA model; MVLR model; Online early warning
资金
- National Natural Science Foundation of China [51425902, 51279196]
- Nanjing Hydraulic Research Institute [Y913017]
- Chinese Academy of Sciences [KZZD-EW-10-02]
Algal blooms are commonly observed in freshwater and coastal areas, causing significant damage to drinking water and aquaculture production. Predictive models are effective for algal bloom forecasting and management. In this paper, an auto-regressive integrated moving average (ARIMA) model was developed to predict daily chlorophyll a (Chl a) concentrations, using data from Taihu Lake in China. For comparison, a multivariate linear regression (MVLR) model was also established to predict daily Chl a concentrations using the same data. Results showed that the ARIMA model generally performed better than the MVLR model with respect to the absolute error of peak value, root mean square error and index of agreement. Because the ARIMA model needs only one input variable, it shows greater applicability as an algal bloom early warning system using online sensors of Chl a. (C) 2015 Elsevier B.V. All rights reserved.
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