Nitrogen prediction in the Great Barrier Reef using finite element analysis with deep neural networks. (April 2022)

• Record Type: Journal Article
• Authors: Jahanbakht, Mohammad; Xiang, Wei; Robson, Barbara; Azghadi, Mostafa Rahimi
• Is Part Of: Environmental modelling & software. Volume 150(2022)
• Publication Date: 2022-04
• Subjects: Machine Learning -- Deep neural networks -- Finite element analysis -- Partial differential equation -- Total nitrogen forecasting -- Next-frame prediction -- Great barrier reef -- Physics-informed neural network -- eReefs modelling suite; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105311
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The corals of the Great Barrier Reef (GBR) in Australia are under pressure from contaminants including nitrogen entering the sea. To provide decision support in reaching target water quality outcomes, development of a nitrogen forecasting model may be useful. Here, we propose a new technique that considers the whole GBR as a frame and treats forecasting of nitrogen as a next-frame prediction task, to produce spatial maps of nitrogen over the whole GBR at forecast time-steps. To achieve this, we design an innovative Deep Neural Network (DNN) inspired by the Finite Element (FE) analysis concept. In our proposed method, the GBR area is meshed into small elements with pre-calculated stiffness matrices first. Next, both the stiffness matrices and the nitrogen values of each element are fed into the designed DNN for element-wise nitrogen prediction. The final result is then gained by attaching separate outputs of each element. Unlike other next-frame prediction models, our FE-DNN model generates accurate forecasts with unblurred prediction frames. We demonstrate that our model is the first to provide nitrogen forecasts for the entire GBR with low Mean Square Error (MSE), while generating a high-resolution prediction frame. The proposed model is applicable to other environmental modelling applications that are governed by Partial Differential Equations (PDE), e.g., sea temperature prediction and sediment distribution forecasting. Nonetheless, no knowledge of the underlying PDEs is required to use our DNN-based model. Our method can produce accurate forecasting predictions by leveraging existing hindcasting simulation models. Highlights: Finite element analysis is incorporated in deep neural networks to form a new FE-DNN model. Nitrogen distribution in the wide Great Barrier Reef is forecasted using the proposed FE-DNN. The required stiffness matrices are numerically calculated. The resulting next-frame predicting model exhibits high resolution and high accuracy. FE-DNN is applicable to other environmental models that are governed by partial differential equations.