Dynamic transfer partial least squares for domain adaptive regression

The traditional soft sensor models are based on the independent and identical distribution assumption, which are difficult to adapt to changes in data distribution under multiple operating conditions, resulting in model performance deterioration. The domain adaptive transfer learning methods learn knowledge in different domains by means of distribution alignment, which can reduce the impact of data distribution differences, and effectively improve the generalization ability of the model. However, most of the existing models established by domain adaptation methods are static models, which cannot reflect the dynamic characteristics of the system, and have limited prediction accuracy when applied to dynamic system modeling under multiple operating conditions. The dynamic system modeling methods can effectively extract the dynamic characteristics of the data, but they cannot deal with the concept drift problem caused by the change of data distribution. This paper proposes a new dynamic transfer partial least squares method, which maps the high-dimensional process data into the low-dimensional latent variable subspace, establishes the dynamic regression relationship between the latent variables and the labels, and realizes the systematic dynamic modeling, at the same time, the model adds regular terms for distribution alignment and structure preservation, which realizes dynamic alignment of data distribution difference. The effectiveness of the proposed method is validated on three publicly available industrial process datasets.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport Engineering and Logistic

Tidal-flat reclamation aggravates potential risk from storm impacts

A better understanding of how tidal-flat reclamation changes the flood hazard is critical for climate-proofing coastal flood defense design of heavily urbanized areas. Since the 1950s, large-scale reclamation has been performed along the Shanghai coast, China, to fulfill the land demands of city expansion. We now show that the loss of tidal flats may have resulted in harmful impacts of coastal storm flooding. Using the foreshore profiles measured before and after reclamation (i.e., wide vs. narrow tidal flat), we determined the long-term changes in flood risk using a numerical model that combines extreme tidal level and wave overtopping analysis. Results show that wide tidal flats in front of a seawall provide efficient wave damping even during extreme water levels. Reclamation of these tidal flats substantially increased wave heights and correspondingly reduced the return period of a specific storm. As a result, estimates of overtopping are aggravated by more than 80% for the varying return periods examined. It is concluded that the disasters of coastal flooding after the 1997 tidal-flat reclamation in Hangzhou Bay, China are a consequence of both anthropogenic and natural activities. Moreover, our model calculations provide an equation describing the equivalent dike height needed to compensate for the loss of every km tidal flat of a certain elevation, and vice versa. For example, for every km of tidal flat ranging from high marsh to bare tidal flat that is being regained, the dike can be lowered by 0.84 m–0.67 m, when designing for a 1 in 200 years storm event. Overall, we suggest that wide tidal flats are ideally restored in front of dikes, and that when tidal areas are reclaimed, the seawall height is raised as part of the intertidal reclamation procedure. Using such an equivalent protection standard is relevant to designing hybrid flood defense system worldwide.Accepted author manuscriptHydraulic Structures and Flood Ris