Overflow Capacity Prediction of Pumping Station Based on Data Drive Article Swipe
Tiantian Guo
,
Jianzhuo Yan
,
Jianhui Chen
,
Yongchuan Yu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/w15132380
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/w15132380
In recent years, the information requirements of pumping stations have become higher and higher. The prediction of overflow capacity can provide important reference for flood carrying capacity, water resource scheduling and water safety. In order to improve the accuracy, stability and generalization ability of the model, a BiGRU–ARIMA data-driven method based on self-attention mechanism is proposed to predict the flow capacity of the pump station. Bidirectional gated recurrent unit (BiGRU), a variant of cyclic neural network (RNN), can not only deal with nonlinear components well, but also deal with the problem of insufficient dependence over long distances and has a simple structure. Autoregressive integrated moving average (ARIMA) has the advantage of being sensitive to linear components. Firstly, the characteristics of the pre-processed pump station data are selected and screened through Pearson correlation coefficient and a self-attention mechanism. Then, a bi-directional gated recurrent unit (BiGRU) is used to process the nonlinear components of the data, and a dropout layer is added to avoid overfitting phenomena. We extract the linear features of the obtained error terms using the ARIMA model and use them as correction items to correct the prediction results of the BiGRU model. Finally, we obtain the prediction results of the overflow and water level. The variation characteristics of overdischarge are analyzed by the relation of flow and water level. In this paper, the actual production data of a Grade 9 pumping station of Miyun Reservoir is taken as an example to verify the validity of the model. Model performance is evaluated according to mean absolute error (MAE), mean absolute percentage error (MAPE) and linear regression correlation coefficient (R2). The experimental results show that, compared with the single ARIMAX, BiGRU model and BP neural network, the SA–BiGRU–ARIMA hybrid prediction model has a better prediction effect than other data-driven models.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/w15132380
- https://www.mdpi.com/2073-4441/15/13/2380/pdf?version=1688117211
- OA Status
- gold
- Cited By
- 2
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4382202498
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4382202498Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/w15132380Digital Object Identifier
- Title
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Overflow Capacity Prediction of Pumping Station Based on Data DriveWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-28Full publication date if available
- Authors
-
Tiantian Guo, Jianzhuo Yan, Jianhui Chen, Yongchuan YuList of authors in order
- Landing page
-
https://doi.org/10.3390/w15132380Publisher landing page
- PDF URL
-
https://www.mdpi.com/2073-4441/15/13/2380/pdf?version=1688117211Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2073-4441/15/13/2380/pdf?version=1688117211Direct OA link when available
- Concepts
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Overfitting, Autoregressive integrated moving average, Computer science, Nonlinear system, Artificial neural network, Autoregressive model, Generalization, Data mining, Artificial intelligence, Mathematics, Statistics, Machine learning, Time series, Quantum mechanics, Mathematical analysis, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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41Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.of | 6, 16, 43, 61, 72, 91, 110, 119, 151, 169, 189, 199, 208, 215, 227, 233, 245 |
| abstract_inverted_index.on | 51 |
| abstract_inverted_index.to | 35, 56, 113, 146, 160, 184, 241, 253 |
| abstract_inverted_index.we | 194 |
| abstract_inverted_index.The | 14, 205, 269 |
| abstract_inverted_index.and | 12, 30, 40, 97, 127, 133, 154, 178, 202, 217, 263, 281 |
| abstract_inverted_index.are | 125, 210 |
| abstract_inverted_index.but | 85 |
| abstract_inverted_index.can | 19, 77 |
| abstract_inverted_index.for | 23 |
| abstract_inverted_index.has | 98, 107, 290 |
| abstract_inverted_index.not | 78 |
| abstract_inverted_index.the | 3, 37, 44, 58, 62, 89, 108, 117, 120, 148, 152, 166, 170, 175, 186, 190, 196, 200, 213, 223, 243, 246, 276, 285 |
| abstract_inverted_index.use | 179 |
| abstract_inverted_index.also | 86 |
| abstract_inverted_index.data | 124, 226 |
| abstract_inverted_index.deal | 80, 87 |
| abstract_inverted_index.flow | 59, 216 |
| abstract_inverted_index.have | 9 |
| abstract_inverted_index.long | 95 |
| abstract_inverted_index.mean | 254, 258 |
| abstract_inverted_index.only | 79 |
| abstract_inverted_index.over | 94 |
| abstract_inverted_index.pump | 63, 122 |
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| abstract_inverted_index.than | 295 |
| abstract_inverted_index.them | 180 |
| abstract_inverted_index.this | 221 |
| abstract_inverted_index.unit | 68, 142 |
| abstract_inverted_index.used | 145 |
| abstract_inverted_index.with | 81, 88, 275 |
| abstract_inverted_index.(R2). | 268 |
| abstract_inverted_index.ARIMA | 176 |
| abstract_inverted_index.BiGRU | 191, 279 |
| abstract_inverted_index.Grade | 229 |
| abstract_inverted_index.Miyun | 234 |
| abstract_inverted_index.Model | 248 |
| abstract_inverted_index.Then, | 137 |
| abstract_inverted_index.added | 159 |
| abstract_inverted_index.avoid | 161 |
| abstract_inverted_index.based | 50 |
| abstract_inverted_index.being | 111 |
| abstract_inverted_index.data, | 153 |
| abstract_inverted_index.error | 172, 256, 261 |
| abstract_inverted_index.flood | 24 |
| abstract_inverted_index.gated | 66, 140 |
| abstract_inverted_index.items | 183 |
| abstract_inverted_index.layer | 157 |
| abstract_inverted_index.model | 177, 280, 289 |
| abstract_inverted_index.order | 34 |
| abstract_inverted_index.other | 296 |
| abstract_inverted_index.taken | 237 |
| abstract_inverted_index.terms | 173 |
| abstract_inverted_index.that, | 273 |
| abstract_inverted_index.using | 174 |
| abstract_inverted_index.water | 27, 31, 203, 218 |
| abstract_inverted_index.well, | 84 |
| abstract_inverted_index.(MAE), | 257 |
| abstract_inverted_index.(MAPE) | 262 |
| abstract_inverted_index.(RNN), | 76 |
| abstract_inverted_index.actual | 224 |
| abstract_inverted_index.become | 10 |
| abstract_inverted_index.better | 292 |
| abstract_inverted_index.cyclic | 73 |
| abstract_inverted_index.effect | 294 |
| abstract_inverted_index.higher | 11 |
| abstract_inverted_index.hybrid | 287 |
| abstract_inverted_index.level. | 204, 219 |
| abstract_inverted_index.linear | 114, 167, 264 |
| abstract_inverted_index.method | 49 |
| abstract_inverted_index.model, | 45 |
| abstract_inverted_index.model. | 192, 247 |
| abstract_inverted_index.moving | 104 |
| abstract_inverted_index.neural | 74, 283 |
| abstract_inverted_index.obtain | 195 |
| abstract_inverted_index.paper, | 222 |
| abstract_inverted_index.recent | 1 |
| abstract_inverted_index.simple | 100 |
| abstract_inverted_index.single | 277 |
| abstract_inverted_index.verify | 242 |
| abstract_inverted_index.years, | 2 |
| abstract_inverted_index.(ARIMA) | 106 |
| abstract_inverted_index.(BiGRU) | 143 |
| abstract_inverted_index.ARIMAX, | 278 |
| abstract_inverted_index.Pearson | 130 |
| abstract_inverted_index.ability | 42 |
| abstract_inverted_index.average | 105 |
| abstract_inverted_index.correct | 185 |
| abstract_inverted_index.dropout | 156 |
| abstract_inverted_index.example | 240 |
| abstract_inverted_index.extract | 165 |
| abstract_inverted_index.higher. | 13 |
| abstract_inverted_index.improve | 36 |
| abstract_inverted_index.models. | 298 |
| abstract_inverted_index.network | 75 |
| abstract_inverted_index.predict | 57 |
| abstract_inverted_index.problem | 90 |
| abstract_inverted_index.process | 147 |
| abstract_inverted_index.provide | 20 |
| abstract_inverted_index.pumping | 7, 231 |
| abstract_inverted_index.results | 188, 198, 271 |
| abstract_inverted_index.safety. | 32 |
| abstract_inverted_index.station | 123, 232 |
| abstract_inverted_index.through | 129 |
| abstract_inverted_index.variant | 71 |
| abstract_inverted_index.(BiGRU), | 69 |
| abstract_inverted_index.Finally, | 193 |
| abstract_inverted_index.Firstly, | 116 |
| abstract_inverted_index.absolute | 255, 259 |
| abstract_inverted_index.analyzed | 211 |
| abstract_inverted_index.capacity | 18, 60 |
| abstract_inverted_index.carrying | 25 |
| abstract_inverted_index.compared | 274 |
| abstract_inverted_index.features | 168 |
| abstract_inverted_index.network, | 284 |
| abstract_inverted_index.obtained | 171 |
| abstract_inverted_index.overflow | 17, 201 |
| abstract_inverted_index.proposed | 55 |
| abstract_inverted_index.relation | 214 |
| abstract_inverted_index.resource | 28 |
| abstract_inverted_index.screened | 128 |
| abstract_inverted_index.selected | 126 |
| abstract_inverted_index.station. | 64 |
| abstract_inverted_index.stations | 8 |
| abstract_inverted_index.validity | 244 |
| abstract_inverted_index.Reservoir | 235 |
| abstract_inverted_index.according | 252 |
| abstract_inverted_index.accuracy, | 38 |
| abstract_inverted_index.advantage | 109 |
| abstract_inverted_index.capacity, | 26 |
| abstract_inverted_index.distances | 96 |
| abstract_inverted_index.evaluated | 251 |
| abstract_inverted_index.important | 21 |
| abstract_inverted_index.mechanism | 53 |
| abstract_inverted_index.nonlinear | 82, 149 |
| abstract_inverted_index.recurrent | 67, 141 |
| abstract_inverted_index.reference | 22 |
| abstract_inverted_index.sensitive | 112 |
| abstract_inverted_index.stability | 39 |
| abstract_inverted_index.variation | 206 |
| abstract_inverted_index.components | 83, 150 |
| abstract_inverted_index.correction | 182 |
| abstract_inverted_index.dependence | 93 |
| abstract_inverted_index.integrated | 103 |
| abstract_inverted_index.mechanism. | 136 |
| abstract_inverted_index.percentage | 260 |
| abstract_inverted_index.phenomena. | 163 |
| abstract_inverted_index.prediction | 15, 187, 197, 288, 293 |
| abstract_inverted_index.production | 225 |
| abstract_inverted_index.regression | 265 |
| abstract_inverted_index.scheduling | 29 |
| abstract_inverted_index.structure. | 101 |
| abstract_inverted_index.coefficient | 132, 267 |
| abstract_inverted_index.components. | 115 |
| abstract_inverted_index.correlation | 131, 266 |
| abstract_inverted_index.data-driven | 48, 297 |
| abstract_inverted_index.information | 4 |
| abstract_inverted_index.overfitting | 162 |
| abstract_inverted_index.performance | 249 |
| abstract_inverted_index.experimental | 270 |
| abstract_inverted_index.insufficient | 92 |
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| abstract_inverted_index.Bidirectional | 65 |
| abstract_inverted_index.overdischarge | 209 |
| abstract_inverted_index.pre-processed | 121 |
| abstract_inverted_index.Autoregressive | 102 |
| abstract_inverted_index.bi-directional | 139 |
| abstract_inverted_index.generalization | 41 |
| abstract_inverted_index.self-attention | 52, 135 |
| abstract_inverted_index.characteristics | 118, 207 |
| abstract_inverted_index.SA–BiGRU–ARIMA | 286 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5041923179 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.8600000143051147 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.52698149 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |