Online forecasting chlorophyll a concentrations by an auto-regressive integrated moving average model: Feasibilities and potentials

•An ARIMA model was developed to forecast chlorophyll a concentrations.•First order derivative well described daily changes in chlorophyll a concentrations.•The ARIMA model performed relatively better than the multi-variable regression model.•The ARIMA model provides great potential for online early...

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Veröffentlicht in:Harmful algae 2015-03, Vol.43, p.58-65
Hauptverfasser: Chen, Qiuwen, Guan, Tiesheng, Yun, Liu, Li, Ruonan, Recknagel, Friedrich
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container_end_page 65
container_issue
container_start_page 58
container_title Harmful algae
container_volume 43
creator Chen, Qiuwen
Guan, Tiesheng
Yun, Liu
Li, Ruonan
Recknagel, Friedrich
description •An ARIMA model was developed to forecast chlorophyll a concentrations.•First order derivative well described daily changes in chlorophyll a concentrations.•The ARIMA model performed relatively better than the multi-variable regression model.•The ARIMA model provides great potential for online early warning of algal blooms. 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.
doi_str_mv 10.1016/j.hal.2015.01.002
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subjects Algal bloom
ARIMA model
Freshwater
Marine
MVLR model
Online early warning
title Online forecasting chlorophyll a concentrations by an auto-regressive integrated moving average model: Feasibilities and potentials
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