File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: The Extended Kalman Filter for short term prediction of algal bloom dynamics

TitleThe Extended Kalman Filter for short term prediction of algal bloom dynamics
Authors
KeywordsExtended Kalman Filter
Fish culture zone
Real time forecast
Algal bloom
Issue Date2008
PublisherSpringer Berlin Heidelberg
Citation
16th IAHR-APD Congress and 3rd Symposium of IAHR-ISHS, 2008. In Advances in Water Resources and Hydraulic Engineering, 2009, p. 513-517 How to Cite?
AbstractAn Extended Kalman Filter (EKF) is incorporated into a water quality model to assimilate high frequency field observations of algal bloom and dissolved oxygen dynamics in a marine fish culture zone (FCZ). The weakly-flushed fish culture zone is modeled as a well-mixed system with numerically determined flushing rate. The ecosystem model incorporates phytoplankton growth kinetics, nutrient uptake, photosynthetic production, nutrient sources from organic fish farm loads, and nutrient exchange with a sediment bed layer. Supported by the high frequency observations (sampling interval Δt = 1 day, 1 hour, and 2 weeks for chlorophyll and dissolved oxygen, hydro- meteorological parameters, and nutrient, respectively), a number of algal blooms observed at Lamma Island of Hong Kong are used to assess the performance of the EKF. Daily chlorophyll levels estimated by the EKF are compared with field observations and the unfiltered deterministic model prediction for different algal bloom events. The data assimilation with different observation lead-times is also studied. It is found that the EKF estimate well captures the nonlinear error evolution in time and gives good predictions of short term algal bloom dynamics up to a lead-time of 2 or 3 days. The present study is the first time the Extended Kalman Filter is successfully applied to forecast an entire algal bloom cycle, suggesting the possibility of using EKF for real time forecast of algal bloom dynamics.
Persistent Identifierhttp://hdl.handle.net/10722/63178
ISBN

 

DC FieldValueLanguage
dc.contributor.authorLee, JHWen_HK
dc.contributor.authorMao, Jen_HK
dc.contributor.authorChoi, DKWen_HK
dc.date.accessioned2010-07-13T04:17:58Z-
dc.date.available2010-07-13T04:17:58Z-
dc.date.issued2008en_HK
dc.identifier.citation16th IAHR-APD Congress and 3rd Symposium of IAHR-ISHS, 2008. In Advances in Water Resources and Hydraulic Engineering, 2009, p. 513-517en_HK
dc.identifier.isbn978-3-540-89464-3-
dc.identifier.urihttp://hdl.handle.net/10722/63178-
dc.description.abstractAn Extended Kalman Filter (EKF) is incorporated into a water quality model to assimilate high frequency field observations of algal bloom and dissolved oxygen dynamics in a marine fish culture zone (FCZ). The weakly-flushed fish culture zone is modeled as a well-mixed system with numerically determined flushing rate. The ecosystem model incorporates phytoplankton growth kinetics, nutrient uptake, photosynthetic production, nutrient sources from organic fish farm loads, and nutrient exchange with a sediment bed layer. Supported by the high frequency observations (sampling interval Δt = 1 day, 1 hour, and 2 weeks for chlorophyll and dissolved oxygen, hydro- meteorological parameters, and nutrient, respectively), a number of algal blooms observed at Lamma Island of Hong Kong are used to assess the performance of the EKF. Daily chlorophyll levels estimated by the EKF are compared with field observations and the unfiltered deterministic model prediction for different algal bloom events. The data assimilation with different observation lead-times is also studied. It is found that the EKF estimate well captures the nonlinear error evolution in time and gives good predictions of short term algal bloom dynamics up to a lead-time of 2 or 3 days. The present study is the first time the Extended Kalman Filter is successfully applied to forecast an entire algal bloom cycle, suggesting the possibility of using EKF for real time forecast of algal bloom dynamics.-
dc.languageengen_HK
dc.publisherSpringer Berlin Heidelbergen_HK
dc.relation.ispartofAdvances in Water Resources and Hydraulic Engineering-
dc.subjectExtended Kalman Filter-
dc.subjectFish culture zone-
dc.subjectReal time forecast-
dc.subjectAlgal bloom-
dc.titleThe Extended Kalman Filter for short term prediction of algal bloom dynamicsen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailLee, JHW: hreclhw@hku.hken_HK
dc.identifier.emailMao, J: jingqm@gmail.comen_HK
dc.identifier.emailChoi, DKW: choidkw@hkucc.hku.hken_HK
dc.identifier.authorityLee, JHW=rp00061en_HK
dc.identifier.authorityChoi, DKW=rp00107en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-540-89465-0_89-
dc.identifier.hkuros155279en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats