Incorporation of artificial neural networks and data assimilation techniques into a third-generation wind–wave model for wave forecasting

Abstract

Although the third-generation formulation of the ocean wave model describes the wave generation, dissipation and nonlinear interaction processes explicitly, many empirical parameters exist in the model which have to be determined experimentally. With the advance in oceanographic remote-sensing techniques, information on oceanic parameters including significant wave height (SWH) can be obtained daily by satellite altimeters. The assimilation of these data into the wave model provides a way of improving the hindcasting results. However, for wave forecasting, no altimeter data exist during the forecasting period, by definition. To improve the forecasting accuracy of the wave model, Artificial Neural Networks (ANN) are introduced to mimic the errors introduced by the wave model. This is achieved by training the ANN using the wave model output as input, and the results after data assimilation as the targeted output. The trained ANN is then used as a post-processor of the output from the wave model. The proposed method has been applied in wave simulation in the northwestern Pacific Ocean. The statistical interpolation method is used to assimilate the altimeter data into the wave model output and a back-propagation ANN is used to mimic the relation between the wave model outputs with or without data assimilation. The results show that an apparent improvement in the accuracy of forecasting can be obtained.

Estimation of scour around submarine pipelines with Artificial Neural Network

“…These techniques have also been used in other marine and coastal engineering studies such as nearshore processes and coastal modeling [37,7], wind-wave estimation (e.g., [9,13,42,27]), ecological system analysis in coastal waters [34,8], and current velocity forecasting in straits [1].…”

Combining deterministic modelling with artificial neural networks for suspended sediment estimates

“…[10,11]) and assimilation (e.g. [12][13][14]). Comparisons between outcomes of ANNs and conventional modelling techniques (e.g.…”

Wave data assimilation using a hybrid approach in the Persian Gulf

“…Similar results are reported in Galanis et al (2009) in that they employed some statistical tools to extend the impact of data assimilation on ocean wave prediction. In another study, Zhang et al (2006) incorporated artificial neural networks and data assimilation techniques into the WAM model for wave forecasting. They assimilated altimeter significant wave height (H s ) data into the wave model by the statistical interpolation method to improve the accuracy of wave height prediction.…”