File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1016/j.ecolmodel.2006.06.017
- Scopus: eid_2-s2.0-37849189766
- WOS: WOS:000243600800011
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Modelling algal blooms using vector autoregressive model with exogenous variables and long memory filter
Title | Modelling algal blooms using vector autoregressive model with exogenous variables and long memory filter |
---|---|
Authors | |
Keywords | Algal blooms Early warning system Long range dependence Red-tide Time series forecasting VARX modelling |
Issue Date | 2007 |
Publisher | Elsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/ecolmodel |
Citation | Ecological Modelling, 2007, v. 200 n. 1-2, p. 130-138 How to Cite? |
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. © 2006 Elsevier B.V. All rights reserved. |
Persistent Identifier | http://hdl.handle.net/10722/172442 |
ISSN | 2023 Impact Factor: 2.6 2023 SCImago Journal Rankings: 0.824 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lui, GCS | en_HK |
dc.contributor.author | Li, WK | en_HK |
dc.contributor.author | Leung, KMY | en_HK |
dc.contributor.author | Lee, JHW | en_HK |
dc.contributor.author | Jayawardena, AW | en_HK |
dc.date.accessioned | 2012-10-30T06:22:33Z | - |
dc.date.available | 2012-10-30T06:22:33Z | - |
dc.date.issued | 2007 | en_HK |
dc.identifier.citation | Ecological Modelling, 2007, v. 200 n. 1-2, p. 130-138 | en_HK |
dc.identifier.issn | 0304-3800 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/172442 | - |
dc.description.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. © 2006 Elsevier B.V. All rights reserved. | en_HK |
dc.language | eng | en_US |
dc.publisher | Elsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/ecolmodel | en_HK |
dc.relation.ispartof | Ecological Modelling | en_HK |
dc.rights | Ecological Modelling. Copyright © Elsevier BV. | - |
dc.subject | Algal blooms | en_HK |
dc.subject | Early warning system | en_HK |
dc.subject | Long range dependence | en_HK |
dc.subject | Red-tide | en_HK |
dc.subject | Time series forecasting | en_HK |
dc.subject | VARX modelling | en_HK |
dc.title | Modelling algal blooms using vector autoregressive model with exogenous variables and long memory filter | en_HK |
dc.type | Article | en_HK |
dc.identifier.email | Lui, GCS: csglui@hku.hk | en_HK |
dc.identifier.email | Li, WK: hrntlwk@hkucc.hku.hk | en_HK |
dc.identifier.email | Leung, KMY: kmyleung@hku.hk | en_HK |
dc.identifier.email | Lee, JHW: hreclhw@hku.hk | en_HK |
dc.identifier.authority | Lui, GCS=rp00755 | en_HK |
dc.identifier.authority | Li, WK=rp00741 | en_HK |
dc.identifier.authority | Leung, KMY=rp00733 | en_HK |
dc.identifier.authority | Lee, JHW=rp00061 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1016/j.ecolmodel.2006.06.017 | en_HK |
dc.identifier.scopus | eid_2-s2.0-37849189766 | en_HK |
dc.identifier.hkuros | 125140 | - |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-37849189766&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 200 | en_HK |
dc.identifier.issue | 1-2 | en_HK |
dc.identifier.spage | 130 | en_HK |
dc.identifier.epage | 138 | en_HK |
dc.identifier.isi | WOS:000243600800011 | - |
dc.publisher.place | Netherlands | en_HK |
dc.identifier.scopusauthorid | Lui, GCS=8613288600 | en_HK |
dc.identifier.scopusauthorid | Li, WK=14015971200 | en_HK |
dc.identifier.scopusauthorid | Leung, KMY=7401860738 | en_HK |
dc.identifier.scopusauthorid | Lee, JHW=36078318900 | en_HK |
dc.identifier.scopusauthorid | Jayawardena, AW=7005049253 | en_HK |
dc.identifier.issnl | 0304-3800 | - |