Author(s): Bernard B. Hsieh; Thad C. Pratt
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Keywords: No Keywords
Abstract: Field data collection is expensive and plays a main part in building a numerical modeling system. However, the missing gaps and abnormal recorded data due to instrumental failure curtail the capability of modeling effort. This deficit can be overcome by the system simulation techniques, such as artificial neural networks (ANNs). Several different data recovery patterns, namely, self-recovery, neighboring station recovery, and multivariate parameter recovery, were defined and solved by the ANN modeling technique. The Biscayne Bay tidal system was used to demonstrate this approach. The results indicated that the recovery reliability depends on the parameter characteristics. The performance of missing window size and its location in the time series were discussed. The partially recurrent network algorithm was found to be the most accurate data recovery system for this application.
Year: 2001