Multi-task learning using non-linear autoregressive models and recurrent neural networks for tide level forecasting Article Swipe
Nerfita Nikentari
,
Hua‐Liang Wei
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.11591/ijece.v14i1.pp960-970
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.11591/ijece.v14i1.pp960-970
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.11591/ijece.v14i1.pp960-970
- https://ijece.iaescore.com/index.php/IJECE/article/download/32901/17152
- OA Status
- diamond
- Cited By
- 2
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388656589
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388656589Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.11591/ijece.v14i1.pp960-970Digital Object Identifier
- Title
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Multi-task learning using non-linear autoregressive models and recurrent neural networks for tide level forecastingWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-11-14Full publication date if available
- Authors
-
Nerfita Nikentari, Hua‐Liang WeiList of authors in order
- Landing page
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https://doi.org/10.11591/ijece.v14i1.pp960-970Publisher landing page
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https://ijece.iaescore.com/index.php/IJECE/article/download/32901/17152Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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-
diamondOpen access status per OpenAlex
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https://ijece.iaescore.com/index.php/IJECE/article/download/32901/17152Direct OA link when available
- Concepts
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Autoregressive model, Task (project management), Computer science, Recurrent neural network, Artificial neural network, Artificial intelligence, Machine learning, Deep learning, Time series, Econometrics, Engineering, Mathematics, Systems engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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35Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.role | 6 |
| abstract_inverted_index.same | 49 |
| abstract_inverted_index.task | 23, 109 |
| abstract_inverted_index.that | 25, 166 |
| abstract_inverted_index.them | 94 |
| abstract_inverted_index.tide | 13, 33, 45, 56, 68, 149 |
| abstract_inverted_index.unit | 132 |
| abstract_inverted_index.used | 42, 54 |
| abstract_inverted_index.with | 82, 114, 141 |
| abstract_inverted_index.(GRU) | 133 |
| abstract_inverted_index.(MTL) | 99 |
| abstract_inverted_index.(STL) | 111 |
| abstract_inverted_index.Three | 121 |
| abstract_inverted_index.based | 30 |
| abstract_inverted_index.built | 29 |
| abstract_inverted_index.gated | 130 |
| abstract_inverted_index.level | 1, 14, 34, 46, 69, 150 |
| abstract_inverted_index.model | 28, 86 |
| abstract_inverted_index.plays | 3 |
| abstract_inverted_index.study | 62, 147 |
| abstract_inverted_index.using | 117 |
| abstract_inverted_index.inputs | 84 |
| abstract_inverted_index.memory | 128 |
| abstract_inverted_index.method | 66, 76 |
| abstract_inverted_index.moving | 80 |
| abstract_inverted_index.neural | 89 |
| abstract_inverted_index.single | 22, 108 |
| abstract_inverted_index.(LSTM), | 129 |
| abstract_inverted_index.(RNNs), | 91 |
| abstract_inverted_index.Current | 12 |
| abstract_inverted_index.another | 59 |
| abstract_inverted_index.average | 81 |
| abstract_inverted_index.compare | 107 |
| abstract_inverted_index.methods | 16 |
| abstract_inverted_index.models. | 120, 144 |
| abstract_inverted_index.namely, | 125 |
| abstract_inverted_index.results | 164 |
| abstract_inverted_index.solving | 21 |
| abstract_inverted_index.usually | 18 |
| abstract_inverted_index.without | 116 |
| abstract_inverted_index.(BiLSTM) | 137 |
| abstract_inverted_index.(NARMAX) | 85 |
| abstract_inverted_index.combines | 77 |
| abstract_inverted_index.designed | 103 |
| abstract_inverted_index.employed | 139 |
| abstract_inverted_index.forecast | 44 |
| abstract_inverted_index.learning | 98, 110 |
| abstract_inverted_index.location | 39, 50 |
| abstract_inverted_index.methods. | 175 |
| abstract_inverted_index.multiple | 72 |
| abstract_inverted_index.networks | 90 |
| abstract_inverted_index.proposed | 168 |
| abstract_inverted_index.proposes | 63 |
| abstract_inverted_index.together | 140 |
| abstract_inverted_index.classical | 172 |
| abstract_inverted_index.different | 122, 154 |
| abstract_inverted_index.exogenous | 83 |
| abstract_inverted_index.important | 5 |
| abstract_inverted_index.location. | 60 |
| abstract_inverted_index.locations | 73, 156 |
| abstract_inverted_index.nonlinear | 78 |
| abstract_inverted_index.performed | 105 |
| abstract_inverted_index.problems, | 24 |
| abstract_inverted_index.recurrent | 88, 131 |
| abstract_inverted_index.variants, | 124 |
| abstract_inverted_index.conducted. | 162 |
| abstract_inverted_index.framework. | 100 |
| abstract_inverted_index.individual | 38 |
| abstract_inverted_index.locations) | 160 |
| abstract_inverted_index.management | 9 |
| abstract_inverted_index.multi-task | 97 |
| abstract_inverted_index.non-linear | 118, 142 |
| abstract_inverted_index.outperform | 170 |
| abstract_inverted_index.prediction | 70, 174 |
| abstract_inverted_index.short-term | 127 |
| abstract_inverted_index.Experiments | 101 |
| abstract_inverted_index.demonstrate | 165 |
| abstract_inverted_index.forecasting | 2, 15, 57, 151 |
| abstract_inverted_index.implemented | 19 |
| abstract_inverted_index.single-task | 173 |
| abstract_inverted_index.Experimental | 163 |
| abstract_inverted_index.development. | 11 |
| abstract_inverted_index.geographical | 155 |
| abstract_inverted_index.incorporates | 93 |
| abstract_inverted_index.architectures | 169 |
| abstract_inverted_index.bidirectional | 135 |
| abstract_inverted_index.environmental | 8 |
| abstract_inverted_index.autoregressive | 79, 119, 143 |
| abstract_inverted_index.simultaneously. | 74 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.55177151 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |