1. ERDC Knowledge Core
  2. Engineer Research and Development Center (ERDC)
  3. Coastal and Hydraulics Laboratory (CHL) - (before 9/1981 - present)
  4. Publication
  5. Technical Note
Please use this identifier to cite or link to this item: https://hdl.handle.net/11681/1943
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dc.contributor.authorHsieh, Bernard B. (Bernard Bor-Nian), 1949-en_US
dc.contributor.authorPratt, Thad C.en_US
dc.creatorCoastal and Hydraulics Laboratory (U.S.)en_US
dc.creatorCoastal Inlets Research Program (U.S.)en_US
dc.date.accessioned2016-03-11T00:09:35Zen_US
dc.date.available2016-03-11T00:09:35Zen_US
dc.date.issued2001-09en_US
dc.identifier.govdocERDC/CHL CHETN-IV-38en_US
dc.identifier.urihttp://hdl.handle.net/11681/1943en_US
dc.descriptionTechnical Noteen_US
dc.description.abstractPURPOSE: The field data collection program consumes a major portion of a modeling budget. However, due to instrumentation adjustment and failure, the obtained data could be incomplete or producing abnormal recording curves. For instance, complete boundary condition data are often critical to the numerical modeling effort. The data may be unavailable at appropriate points along the computational domain when the modeling design work changes. In addition, the key locations, which usually have high gradient variation in the numerical model, could be partially missing. Therefore, the judgment of engineering design will lose its reliability if sufficient measurement is not available for those points. The problem of estimation of temporal and spatial variation as described requires more advanced techniques to solve both time-delay and nonlinearity features. In this Coastal and Hydraulics Engineering Technical Note (CHETN), Artificial Neural Networks (ANNs) are used to address the missing data recovery problem for the data collection activities for a tidal lagoon, Biscayne Bay, FL.en_US
dc.description.sponsorshipCoastal Inlets Research Program (U.S.)en_US
dc.format.extent10 pages/1.2 MBen_US
dc.format.mediumPDFen_US
dc.language.isoen_USen_US
dc.publisherEngineer Research and Development Center (U.S.)en_US
dc.relationhttp://acwc.sdp.sirsi.net/client/en_US/search/asset/1000397en_US
dc.relation.ispartofseriesTechnical note (Coastal and Hydraulics Engineering (U.S.)) ; no.ERDC/CHL CHETN-IV-38en_US
dc.rightsApproved for public release; distribution is unlimiteden_US
dc.sourceThis Digital Resource was created from scans of the Print Resourceen_US
dc.subjectArtificial neural networksen_US
dc.subjectBiscayne Bay (Fla.)en_US
dc.subjectLagoonen_US
dc.subjectData recovery systemen_US
dc.subjectMultilayer perceptron modelen_US
dc.subjectPerformanceen_US
dc.subjectRecurrent neural networksen_US
dc.subjectTime-lagged neural networksen_US
dc.subjectCoastal Inlets Research Program (U.S.)en_US
dc.titleField data recovery in tidal system using artificial neural networks (ANNs)en_US
dc.typeReporten_US
Appears in Collections:Technical Note
Technical Note

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