Online forecasting chlorophyll a concentrations by an auto-regressive integrated moving average model: Feasibilities and potentials |
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| Institution: | 1. CEER, Nanjing Hydraulic Research Institute, Nanjing 210029, China;2. RCEES, Chinese Academy of Sciences, Beijing 100085, China;3. CUGB, China University of Geosciences, Beijing 100083, China;4. School of Earth and Environmental Sciences, University of Adelaide, Australia;1. Departamento de Física Aplicada, Facultad de Ciencias del Mar, Universidad de Vigo, 36310 Vigo, Spain;2. Unidad de Oceanografía y Fitoplancton, Instituto Tecnológico para el Control del Medio Marino de Galicia (INTECMAR), 36611 Villargacía de Arousa, Spain;1. École Polytechnique de Montreal, Civil, Mineral and Mining Engineering Department, P.O. Box 6079, Station Centre-ville, Montreal, Quebec, Canada H3C 3A7;2. Department of Biological Sciences, Université du Québec à Montréal, C.P. 8888, succ. Centre-ville, Montréal (Québec), Canada H3C 3P8;3. Department of Engineering, Faculty of Agriculture, Dalhousie University, PO Box 550, Truro-Bible Hill (Nova Scotia), Canada B2N 5E3;4. LEESU, Ecole des Ponts ParisTech, Université Paris-Est, 6 et 8 avenue Blaise Pascal, Cité Descartes, 77455 Marne la Vallée Cedex 2, France;5. AgroParisTech, 16 rue Claude Bernard, 75005 Paris, France;1. RCEES, Chinese Academy of Sciences, Beijing 100085, China;2. CEER, Nanjing Hydraulic Research Institute, Nanjing 210029, China;3. School of Earth & Environmental Sciences, University of Adelaide, 5005 Adelaide, SA, Australia;1. Environmental and Resource Studies, Trent University, 1600 West Bank Drive, Peterborough, ON K9J 7B8, Canada;2. Ontario Ministry of the Environment and Climate Change, Dorset Environmental Science Centre, 1026 Bellwood Acres Road, Dorset, ON P0A 1E0, Canada;3. Ontario Ministry of the Environment and Climate Change, Sport Fish and Biomonitoring Unit, 125 Resources Road, Toronto, ON M9P 3V6, Canada;1. Department of Civil and Environmental Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA;2. Department of Environmental Engineering, Pusan National University, Busan 609-735, Republic of Korea;1. GIS Technology Research Center of Resource and Environment in Western China, Ministry of Education, Yunnan Normal University, Yunnan 650500, China;2. School of Tourism and Geographical Science, Yunnan Normal University, Yunnan 650500, China;3. School of Information Science and Technology, Yunnan Normal University, Yunnan 650500, China;4. Dean’s Office, Yunnan Normal University, Yunnan 650500, China |
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| Abstract: | 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. |
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| Keywords: | Algal bloom ARIMA model MVLR model Online early warning |
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