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dc.contributor.authorBounceur, Nabila
dc.contributor.authorHoteit, Ibrahim
dc.contributor.authorKnio, Omar
dc.date.accessioned2020-04-26T07:43:29Z
dc.date.available2020-04-26T07:43:29Z
dc.date.issued2020
dc.identifier.citationBounceur, N., Hoteit, I., & Knio, O. (2020). A Bayesian Structural Time Series Approach for Predicting Red Sea Temperatures. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1–1. doi:10.1109/jstars.2020.2989218
dc.identifier.issn2151-1535
dc.identifier.doi10.1109/JSTARS.2020.2989218
dc.identifier.urihttp://hdl.handle.net/10754/662628
dc.description.abstractSea surface temperature (SST) is a leading factor impacting coral reefs and causing bleaching events in the Red Sea. A long'term prediction of temperature patterns with an estimate of uncertainty is thus essential for environment man- agement of the Red Sea ecosystem. In this work, we build a data'driven Bayesian structural time series model and show its effectiveness in (1) predicting future SST seasons with a high accuracy, and (2) identifying the main predictive factors of future SST variability among a large number of factors including regional SST and large'scale climate indices. The modelling scheme proposed here applies an efficient hierarchical clustering to identify interconnected subregions that display distinct SST variability over the Red Sea, and a Markov Chain Monte Carlo algorithm to simultaneously select the main predictors while the time series model is being trained. In particular, numerical results indicate that monthly SST can be reliably predicted for the five months ahead.
dc.description.sponsorshipThis work was supported by the Office of Sponsored Research (OSR) at King Abdullah University of Science and Technology (KAUST) under the “Virtual Red Sea Initiative” (Grant # REP/1/3268–01–01).
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/9076881/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9076881
dc.rightsThis work is an open access article licensed under a Creative Commons Attribution 4.0 License.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectRed Sea
dc.subjectSee Surface Temperature
dc.subjectHierarchical Clustering
dc.subjectBayesian Structural Time Series
dc.subjectFactor Selection
dc.subjectPredictive modeling
dc.subjectMarkov Chain Monte Carlo
dc.titleA Bayesian Structural Time Series Approach for Predicting Red Sea Temperatures
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
dc.eprint.versionPost-print
dc.identifier.pages1-1
kaust.personBounceur, Nabila
kaust.personHoteit, Ibrahim
kaust.personKnio, Omar
refterms.dateFOA2020-04-26T07:44:20Z
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)


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This work is an open access article  licensed under a Creative Commons Attribution 4.0 License.
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