Nikentari, N. orcid.org/0009-0008-6635-2945 and Wei, H.-L. orcid.org/0000-0002-4704-7346 (2024) Multi-task learning using non-linear autoregressive models and recurrent neural networks for tide level forecasting. International Journal of Electrical and Computer Engineering (IJECE), 14 (1). pp. 960-970. ISSN 2088-8708
Abstract
Tide level forecasting plays an important role in environmental management and development. Current tide level forecasting methods are usually implemented for solving single task problems, that is, a model built based on the tide level data at an individual location is only used to forecast tide level of the same location but is not used for tide forecasting at another location. This study proposes a new method for tide level prediction at multiple locations simultaneously. The method combines nonlinear autoregressive moving average with exogenous inputs (NARMAX) model and recurrent neural networks (RNNs), and incorporates them into a multi-task learning (MTL) framework. Experiments are designed and performed to compare single task learning (STL) and MTL with and without using non-linear autoregressive models. Three different RNN variants, namely, long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (BiLSTM) are employed together with non-linear autoregressive models. A case study on tide level forecasting at many different geographical locations (5 to 11 locations) is conducted. Experimental results demonstrate that the proposed architectures outperform the classical single-task prediction methods.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Author(s). This is an open access article under the CC BY-SA license. (https://creativecommons.org/licenses/by-sa/4.0/) |
Keywords: | Forecasting; Multi-task learning; Nonlinear autoregressive moving average with exogenous model; Recurrent neural network; Tide level |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number NATURAL ENVIRONMENT RESEARCH COUNCIL NE/W005875/1 NATURAL ENVIRONMENT RESEARCH COUNCIL NE/V001787/1 NATURAL ENVIRONMENT RESEARCH COUNCIL NE/V002511/1 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/I011056/1 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL EP/H00453X/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Nov 2023 16:42 |
Last Modified: | 17 Nov 2023 05:21 |
Status: | Published |
Publisher: | Institute of Advanced Engineering and Science |
Refereed: | Yes |
Identification Number: | 10.11591/ijece.v14i1.pp960-970 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:205465 |
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