A combination of the WAVEWATCH III (WW3) model and a modified Holland vortex model is developed and studied in the present work. The Holland 2010 model is modified with two improvements: the first is a new scaling parameter, bs, that is formulated with information about the maximum wind speed (vms) and the typhoon’s forward movement velocity (vt); the second is the introduction of an asymmetric typhoon structure. In order to convert the wind speed, as reconstructed by the modified Holland model, from 1-min averaged wind inputs into 10-min averaged wind inputs to force the WW3 model, a gust factor (gf) is fitted in accordance with practical test cases. Validation against wave buoy data proves that the combination of the two models through the gust factor is robust for the estimation of typhoon waves. The proposed method can simulate typhoon waves efficiently based on easily accessible data sources.
The traditional statistical models for concrete dam deformation do not consider the effective contribution to residual sequence and thus results in the low accuracy of deformation prediction of the monitoring model and in the insufficient prediction of dam deformation behavior.This study proposes a combined model for concrete dam deformation, considering residual correction by frequency division. The horizontal displacement of concrete dams is driven by water pressure, temperature, and aging factors. In this study, an accurate value of water pressure deformation is obtained by finite element calculation, and then the hybrid model is constructed. However, the temperature and aging components hardly obtain accurate information values from the traditional model. Therefore, a combined model is constructed to extract additional detailed information that characterizes the dam deformation from the residual sequence. Ensemble empirical mode decomposition is selected to analyze and denoise the residual sequence adaptively, and the consecutive mean square error is used to determine the boundary points of highand low-frequency components. Then, the combined model based on the extreme learning machine method optimized by particle swarm optimization and seasonal autoregressive integrated moving average is constructed for highand low-frequency sequences. Lastly, the prediction results of different subitems are organically integrated. Results of a case study in engineering demonstrate that the accuracy of the combined model for concrete dam deformation is higher than that of the classical numerical model, and the model can effectively overcome noise interference in the monitoring sequence. The proposed combined model for deformation is reliable for analyzing and judging the dam monitoring time series, which can provide new ideas and reference for the data processing of dam deformation monitoring.
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