A One-Step-Ahead Smoothing-Based Joint Ensemble Kalman Filter for State-Parameter Estimation of Hydrological Models

dc.conference.date2014-11-05 to 2014-11-07
dc.conference.locationCambridge, MA, USA
dc.conference.name1st International Conference on Dynamic Data-Driven Environmental Systems Science, DyDESS 2014
dc.contributor.authorEl Gharamti, Mohamad
dc.contributor.authorAit-El-Fquih, Boujemaa
dc.contributor.authorHoteit, Ibrahim
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.date.accessioned2017-01-02T08:10:20Z
dc.date.available2017-01-02T08:10:20Z
dc.date.issued2015-11-27
dc.date.published-online2015-11-27
dc.date.published-print2015
dc.description.abstractThe ensemble Kalman filter (EnKF) recursively integrates field data into simulation models to obtain a better characterization of the model’s state and parameters. These are generally estimated following a state-parameters joint augmentation strategy. In this study, we introduce a new smoothing-based joint EnKF scheme, in which we introduce a one-step-ahead smoothing of the state before updating the parameters. Numerical experiments are performed with a two-dimensional synthetic subsurface contaminant transport model. The improved performance of the proposed joint EnKF scheme compared to the standard joint EnKF compensates for the modest increase in the computational cost.
dc.identifier.citationGharamti ME, Ait-El-Fquih B, Hoteit I (2015) A One-Step-Ahead Smoothing-Based Joint Ensemble Kalman Filter for State-Parameter Estimation of Hydrological Models. Lecture Notes in Computer Science: 207–214. Available: http://dx.doi.org/10.1007/978-3-319-25138-7_19.
dc.identifier.doi10.1007/978-3-319-25138-7_19
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.journalDynamic Data-Driven Environmental Systems Science
dc.identifier.urihttp://hdl.handle.net/10754/622130
dc.publisherSpringer Nature
dc.titleA One-Step-Ahead Smoothing-Based Joint Ensemble Kalman Filter for State-Parameter Estimation of Hydrological Models
dc.typeConference Paper
display.details.left<span><h5>Type</h5>Conference Paper<br><br><h5>Authors</h5><a href="https://repository.kaust.edu.sa/search?query=orcid.id:0000-0002-7229-8366&spc.sf=dc.date.issued&spc.sd=DESC">El Gharamti, Mohamad</a> <a href="https://orcid.org/0000-0002-7229-8366" target="_blank"><img src="https://repository.kaust.edu.sa/server/api/core/bitstreams/82a625b4-ed4b-40c8-865a-d6a5225a26a4/content" width="16" height="16"/></a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.author=Ait-El-Fquih, Boujemaa,equals">Ait-El-Fquih, Boujemaa</a><br><a href="https://repository.kaust.edu.sa/search?query=orcid.id:0000-0002-3751-4393&spc.sf=dc.date.issued&spc.sd=DESC">Hoteit, Ibrahim</a> <a href="https://orcid.org/0000-0002-3751-4393" target="_blank"><img src="https://repository.kaust.edu.sa/server/api/core/bitstreams/82a625b4-ed4b-40c8-865a-d6a5225a26a4/content" width="16" height="16"/></a><br><br><h5>KAUST Department</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Applied Mathematics and Computational Science Program,equals">Applied Mathematics and Computational Science Program</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Earth Fluid Modeling and Prediction Group,equals">Earth Fluid Modeling and Prediction Group</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Earth Science and Engineering Program,equals">Earth Science and Engineering Program</a><br><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.department=Physical Science and Engineering (PSE) Division,equals">Physical Science and Engineering (PSE) Division</a><br><br><h5>Online Publication Date</h5>2015-11-27<br><br><h5>Print Publication Date</h5>2015<br><br><h5>Date</h5>2015-11-27</span>
display.details.right<span><h5>Abstract</h5>The ensemble Kalman filter (EnKF) recursively integrates field data into simulation models to obtain a better characterization of the model’s state and parameters. These are generally estimated following a state-parameters joint augmentation strategy. In this study, we introduce a new smoothing-based joint EnKF scheme, in which we introduce a one-step-ahead smoothing of the state before updating the parameters. Numerical experiments are performed with a two-dimensional synthetic subsurface contaminant transport model. The improved performance of the proposed joint EnKF scheme compared to the standard joint EnKF compensates for the modest increase in the computational cost.<br><br><h5>Citation</h5>Gharamti ME, Ait-El-Fquih B, Hoteit I (2015) A One-Step-Ahead Smoothing-Based Joint Ensemble Kalman Filter for State-Parameter Estimation of Hydrological Models. Lecture Notes in Computer Science: 207–214. Available: http://dx.doi.org/10.1007/978-3-319-25138-7_19.<br><br><h5>Publisher</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.publisher=Springer Nature,equals">Springer Nature</a><br><br><h5>Journal</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.journal=Dynamic Data-Driven Environmental Systems Science,equals">Dynamic Data-Driven Environmental Systems Science</a><br><br><h5>Conference/Event Name</h5><a href="https://repository.kaust.edu.sa/search?spc.sf=dc.date.issued&spc.sd=DESC&f.conference=1st International Conference on Dynamic Data-Driven Environmental Systems Science, DyDESS 2014,equals">1st International Conference on Dynamic Data-Driven Environmental Systems Science, DyDESS 2014</a><br><br><h5>DOI</h5><a href="https://doi.org/10.1007/978-3-319-25138-7_19">10.1007/978-3-319-25138-7_19</a></span>
kaust.personEl Gharamti, Mohamad
kaust.personAit-El-Fquih, Boujemaa
kaust.personHoteit, Ibrahim
orcid.authorEl Gharamti, Mohamad::0000-0002-7229-8366
orcid.authorAit-El-Fquih, Boujemaa
orcid.authorHoteit, Ibrahim::0000-0002-3751-4393
orcid.id0000-0002-3751-4393
orcid.id0000-0002-7229-8366
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