A novel hybrid machine learning model for rapid assessment of wave and storm surge responses over an extended coastal region - Saviz Naeini, Saeed; Snaiki, Reda (Coastal engineering (Amsterdam), 2024)
Storm surge and waves are responsible for a substantial portion of tropical and extratropical cyclones-related damages. While high-fidelity numerical models have significantly advanced the simulation accuracy of storm surge and waves, they are not practical to be employed for probabilistic analysis, risk assessment or rapid prediction due to their high computational demands. In this study, a novel hybrid model combining dimensionality reduction and data-driven techniques is developed for rapid assessment of waves and storm surge responses over an extended coastal region. Specifically, the hybrid model simultaneously identifies a low-dimensional representation of the high-dimensional spatial system based on a deep autoencoder (DAE) while mapping the storm parameters to the obtained low-dimensional latent space using a deep neural network (DNN). To train the hybrid model, a combined weighted loss function is designed to encourage a balance between DAE and DNN training and achieve the best accuracy. The performance of the hybrid model is evaluated through a case study using the synthetic data from the North Atlantic Comprehensive Coastal Study (NACCS) covering critical regions within New York and New Jersey. In addition, the proposed approach is compared with two decoupled models where the regression model is based on DNN and the reduction techniques are either principal component analysis (PCA) or DAE which are trained separately from the DNN model. High accuracy and computational efficiency are observed for the hybrid model which could be readily implemented as part of early warning systems or probabilistic risk assessment of waves and storm surge. •A novel hybrid model is proposed which can rapidly estimate waves and storm surge responses over an extended coastal region.•The proposed model combines a deep autoencoder (DAE) and a deep neural network (DNN).•The developed model is trained simultaneously based on a designed weighted loss function.•The model was comprehensively tested and validated.•The performance of the hybrid model was compared with conventional decoupled models.