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dc.contributor.authorHsieh, Bernard B. (Bernard Bor-Nian), 1949--
dc.contributor.authorPratt, Thad C.-
dc.descriptionTechnical note-
dc.descriptionPURPOSE: 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.-
dc.publisherCoastal and Hydraulics Laboratory-
dc.publisherEngineer Research and Development Center (U.S.)-
dc.rightsApproved for public release; distribution is unlimited.-
dc.sourceThis Digital Resource was created from scans of the Print Resource.-
dc.subjectArtificial neural networks-
dc.subjectBiscayne Bay, FL-
dc.subjectData recovery system-
dc.subjectMultilayer perceptron model-
dc.subjectRecurrent neural networks-
dc.subjectTime-lagged neural networks-
dc.titleField data recovery in tidal system using artificial neural networks (ANNs)-
Appears in Collections:Technical Note
Technical Note

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