Sediment Prediction in the Great Barrier Reef using Vision Transformer with finite element analysis

Jahanbakht, Mohammad, Xiang, Wei, and Azghadi, Mostafa Rahimi (2022) Sediment Prediction in the Great Barrier Reef using Vision Transformer with finite element analysis. Neural Networks, 152. pp. 311-321.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website: https://doi.org/10.1016/j.neunet.2022.04...
 
2


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.

Item ID: 73872
Item Type: Article (Research - C1)
ISSN: 1879-2782
Related URLs:
Copyright Information: © 2022 Elsevier Ltd. All rights reserved.
Date Deposited: 31 May 2022 23:30
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 50%
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461104 Neural networks @ 50%
SEO Codes: 18 ENVIRONMENTAL MANAGEMENT > 1805 Marine systems and management > 180505 Measurement and assessment of marine water quality and condition @ 50%
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 50%
Downloads: Total: 2
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page