Sediment Prediction in the Great Barrier Reef using Vision Transformer with finite element analysis. (August 2022)

• Record Type: Journal Article
• Authors: Jahanbakht, Mohammad; Xiang, Wei; Azghadi, Mostafa Rahimi
• Is Part Of: Neural networks. Volume 152(2022)
• Page Start: 311
• Page End: 321
• Publication Date: 2022-08
• Subjects: Deep neural networks -- Vision Transformer -- Finite element analysis -- Partial differential equation -- Total sediment forecasting -- Great Barrier Reef; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2022.04.022
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Suspended sediment is a significant threat to the Great Barrier Reef (GBR) ecosystem. This catchment pollutant stems primarily from terrestrial soil erosion. Bulk masses of sediments have potential to propagate from river plumes into the mid-shelf and outer-shelf regions. Existing sediment forecasting methods suffer from the problem of low-resolution predictions, making them unsuitable for wide area coverage. In this paper, a novel sediment distribution prediction model is proposed to augment existing water quality management programs for the GBR. This model is based on the state-of-the-art Transformer network in conjunction with the well-known finite element analysis. For model training, the emerging physics-informed neural network is employed to incorporate both simulated and measured sediment data. Our proposed Finite Element Transformer (FE-Transformer) model offers accurate predictions of sediment across the entire GBR. It provides unblurred outputs, which cannot be achieved with previous next-frame prediction models. This paves a way for accurate forecasting of sediment, which in turn may lead to improved water quality management for the GBR. Highlights: Vision Transformer is proposed for next-frame prediction. Finite element analysis is integrated with the Vision Transformer. Sediment distribution in the GBR is forecasted using the proposed FE-Transformer. PINN is employed to merge sediment PDE solutions with in-situ measured data. The proposed model produces highly accurate unblurred output frames.