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Modelling algal blooms using vector autoregressive model with exogenous variables and long memory filter

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  • Lui, Gilbert C.S.
  • Li, W.K.
  • Leung, Kenneth M.Y.
  • Lee, Joseph H.W.
  • Jayawardena, A.W.

Abstract

Algal blooms (ABs), which commonly occur in urbanised coastal marine environments worldwide, often result in hypoxia and even fish kills. Understanding the mechanism and providing accurate prediction of ABs’ formation and occurrence is of foremost importance in relation to the protection of sensitive marine resources. In this paper, a multivariate time series model, namely the vector autoregressive model with exogenous variables (VARX) and the long memory filter is proposed to model and predict ABs. To evaluate the effectiveness of this VARX model, both daily and 2-h field monitoring data of chlorophyll fluorescence (CHL), dissolved oxygen (DO), total inorganic nitrogen (TIN), water temperature (TEMP), solar radiation (SR) and wind speed (WS) obtained at Kat O, Hong Kong, between February 2000 and March 2003 were employed. Unlike the other data driven approaches, this VARX model not only provides more interpretable effects of specific lags of environmental factors, but also sheds light on the feedback effects of AB on these variables. In general, daily CHL measurements up to 4 days can provide crucial information for predicting algal dynamics, while the VARX model is able to explicitly reveal ecological relationships between CHL and other environmental factors. In addition, the application of long-memory filter can further extract patterns of seasonal variations which is thought to be correspondent to the variation of algal species in Hong Kong water. With a view to providing an early warning signal of AB to fishermen and regulatory authorities, an alarming system was developed based on the VARX model; it could achieve 83% correct prediction of AB occurrences with a lead time of 2.5 days. Concerning the forecast performance of the VARX model, daily forecasting performance is comparatively better than that of artificial neural network models.

Suggested Citation

  • Lui, Gilbert C.S. & Li, W.K. & Leung, Kenneth M.Y. & Lee, Joseph H.W. & Jayawardena, A.W., 2007. "Modelling algal blooms using vector autoregressive model with exogenous variables and long memory filter," Ecological Modelling, Elsevier, vol. 200(1), pages 130-138.
    • Handle: RePEc:eee:ecomod:v:200:y:2007:i:1:p:130-138
    DOI: 10.1016/j.ecolmodel.2006.06.017
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    References listed on IDEAS

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    1. Granger, C. W. J. & Newbold, Paul, 1986. "Forecasting Economic Time Series," Elsevier Monographs, Elsevier, edition 2, number 9780122951831 edited by Shell, Karl.
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    1. Chen, Zhe & Sarkar, Apurbo & Rahman, Airin & Li, Xiaojing & Xia, Xianli, 2022. "Exploring the drivers of green agricultural development (GAD) in China: A spatial association network structure approaches," Land Use Policy, Elsevier, vol. 112(C).
    2. Shen, Jian & Qin, Qubin & Wang, Ya & Sisson, Mac, 2019. "A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading," Ecological Modelling, Elsevier, vol. 398(C), pages 44-54.
    3. Wu, Guozheng & Xu, Zongxue, 2011. "Prediction of algal blooming using EFDC model: Case study in the Daoxiang Lake," Ecological Modelling, Elsevier, vol. 222(6), pages 1245-1252.

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