Optimizing Back-Propagation Neural Network to Retrieve Sea Surface Temperature Based on Improved Sparrow Search Algorithm Article Swipe
Changming Ji
,
Haiyong Ding
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/rs15245722
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/rs15245722
Sea surface temperature (SST) constitutes a pivotal physical parameter in the investigation of atmospheric, oceanic, and air–sea exchange processes. The retrieval of SST through satellite passive microwave (PMW) technology effectively mitigates the interference posed by cloud cover, addressing a longstanding challenge. Nevertheless, conventional functional representations often fall short in capturing the intricate interplay of factors influencing SST. Leveraging neural networks (NNs), known for their adeptness in tackling nonlinear and intricate problems, holds great promise in SST retrieval. Nonetheless, NNs exhibit a high sensitivity to initial weights and thresholds, rendering them susceptible to local optimization issues. In this study, we present a novel machine learning (ML) approach for SST retrieval using PMW measurements, drawing from the Sparrow Search Algorithm (SSA) and Back-Propagation neural network (BPNN) methodologies. The core premise involves the optimization of the BP neural network’s initial weights and thresholds through an enhanced SSA algorithm employing various optimization strategies. This optimization aims to provide superior parameters for the training of the BP neural network. Employing AMSR2 brightness temperature data, sea surface wind speed data, and buoy SST measurements, we construct the ISSA-BP model for sea surface temperature retrieval. The validation of the ISSA-BP model against the test data is conducted and compared against the multiple linear regression (MLR) model, an unoptimized BP model, and an unimproved SSA-BP model. The results manifest an impressive R-squared (R2) value of 0.9918 and a root-mean-square error (RMSE) of 0.8268 °C for the ISSA-BP model, attesting to its superior accuracy. Furthermore, the ISSA-BP model was applied to retrieve global sea surface temperatures on 15 July 2022, yielding an R2 of 0.9926 and an RMSE of 0.7673 °C for the OISST product on the same day, underscoring its excellent concordance. The results indicate that SST can be efficiently and accurately retrieved using the model proposed in this paper, based on satellite PMW measurements. This finding underscores the potential of employing machine learning algorithms for SST retrieval and offers a valuable reference for future studies focusing on the retrieval of other sea surface parameters.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs15245722
- https://www.mdpi.com/2072-4292/15/24/5722/pdf?version=1702527667
- OA Status
- gold
- Cited By
- 6
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389738020
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389738020Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs15245722Digital Object Identifier
- Title
-
Optimizing Back-Propagation Neural Network to Retrieve Sea Surface Temperature Based on Improved Sparrow Search AlgorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-14Full publication date if available
- Authors
-
Changming Ji, Haiyong DingList of authors in order
- Landing page
-
https://doi.org/10.3390/rs15245722Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/15/24/5722/pdf?version=1702527667Direct 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/2072-4292/15/24/5722/pdf?version=1702527667Direct OA link when available
- Concepts
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Artificial neural network, Sea surface temperature, Mean squared error, Buoy, Computer science, Algorithm, Remote sensing, Meteorology, Artificial intelligence, Geology, Mathematics, Geography, Oceanography, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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48Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.15 | 258 |
| abstract_inverted_index.BP | 133, 161, 211 |
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| abstract_inverted_index.R2 | 263 |
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| abstract_inverted_index.is | 198 |
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| abstract_inverted_index.on | 257, 276, 303, 329 |
| abstract_inverted_index.to | 83, 91, 152, 241, 251 |
| abstract_inverted_index.we | 98, 178 |
| abstract_inverted_index.NNs | 78 |
| abstract_inverted_index.PMW | 110, 305 |
| abstract_inverted_index.SSA | 143 |
| abstract_inverted_index.SST | 22, 75, 107, 176, 288, 318 |
| abstract_inverted_index.Sea | 0 |
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| abstract_inverted_index.for | 62, 106, 156, 183, 236, 272, 317, 325 |
| abstract_inverted_index.its | 242, 281 |
| abstract_inverted_index.sea | 169, 184, 254, 334 |
| abstract_inverted_index.the | 10, 31, 50, 114, 129, 132, 157, 160, 180, 191, 195, 203, 237, 246, 273, 277, 296, 310, 330 |
| abstract_inverted_index.was | 249 |
| abstract_inverted_index.°C | 235, 271 |
| abstract_inverted_index.(ML) | 104 |
| abstract_inverted_index.(R2) | 224 |
| abstract_inverted_index.July | 259 |
| abstract_inverted_index.RMSE | 268 |
| abstract_inverted_index.SST. | 56 |
| abstract_inverted_index.This | 149, 307 |
| abstract_inverted_index.aims | 151 |
| abstract_inverted_index.buoy | 175 |
| abstract_inverted_index.core | 126 |
| abstract_inverted_index.data | 197 |
| abstract_inverted_index.day, | 279 |
| abstract_inverted_index.fall | 46 |
| abstract_inverted_index.from | 113 |
| abstract_inverted_index.high | 81 |
| abstract_inverted_index.same | 278 |
| abstract_inverted_index.test | 196 |
| abstract_inverted_index.that | 287 |
| abstract_inverted_index.them | 89 |
| abstract_inverted_index.this | 96, 300 |
| abstract_inverted_index.wind | 171 |
| abstract_inverted_index.(MLR) | 207 |
| abstract_inverted_index.(PMW) | 27 |
| abstract_inverted_index.(SSA) | 118 |
| abstract_inverted_index.(SST) | 3 |
| abstract_inverted_index.2022, | 260 |
| abstract_inverted_index.AMSR2 | 165 |
| abstract_inverted_index.OISST | 274 |
| abstract_inverted_index.based | 302 |
| abstract_inverted_index.cloud | 35 |
| abstract_inverted_index.data, | 168, 173 |
| abstract_inverted_index.error | 231 |
| abstract_inverted_index.great | 72 |
| abstract_inverted_index.holds | 71 |
| abstract_inverted_index.known | 61 |
| abstract_inverted_index.local | 92 |
| abstract_inverted_index.model | 182, 193, 248, 297 |
| abstract_inverted_index.novel | 101 |
| abstract_inverted_index.often | 45 |
| abstract_inverted_index.other | 333 |
| abstract_inverted_index.posed | 33 |
| abstract_inverted_index.short | 47 |
| abstract_inverted_index.speed | 172 |
| abstract_inverted_index.their | 63 |
| abstract_inverted_index.using | 109, 295 |
| abstract_inverted_index.value | 225 |
| abstract_inverted_index.(BPNN) | 123 |
| abstract_inverted_index.(NNs), | 60 |
| abstract_inverted_index.(RMSE) | 232 |
| abstract_inverted_index.0.7673 | 270 |
| abstract_inverted_index.0.8268 | 234 |
| abstract_inverted_index.0.9918 | 227 |
| abstract_inverted_index.0.9926 | 265 |
| abstract_inverted_index.SSA-BP | 216 |
| abstract_inverted_index.Search | 116 |
| abstract_inverted_index.cover, | 36 |
| abstract_inverted_index.future | 326 |
| abstract_inverted_index.global | 253 |
| abstract_inverted_index.linear | 205 |
| abstract_inverted_index.model, | 208, 212, 239 |
| abstract_inverted_index.model. | 217 |
| abstract_inverted_index.neural | 58, 121, 134, 162 |
| abstract_inverted_index.offers | 321 |
| abstract_inverted_index.paper, | 301 |
| abstract_inverted_index.study, | 97 |
| abstract_inverted_index.ISSA-BP | 181, 192, 238, 247 |
| abstract_inverted_index.Sparrow | 115 |
| abstract_inverted_index.against | 194, 202 |
| abstract_inverted_index.applied | 250 |
| abstract_inverted_index.drawing | 112 |
| abstract_inverted_index.exhibit | 79 |
| abstract_inverted_index.factors | 54 |
| abstract_inverted_index.finding | 308 |
| abstract_inverted_index.initial | 84, 136 |
| abstract_inverted_index.issues. | 94 |
| abstract_inverted_index.machine | 102, 314 |
| abstract_inverted_index.network | 122 |
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| abstract_inverted_index.pivotal | 6 |
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| abstract_inverted_index.promise | 73 |
| abstract_inverted_index.provide | 153 |
| abstract_inverted_index.results | 219, 285 |
| abstract_inverted_index.studies | 327 |
| abstract_inverted_index.surface | 1, 170, 185, 255, 335 |
| abstract_inverted_index.through | 23, 140 |
| abstract_inverted_index.various | 146 |
| abstract_inverted_index.weights | 85, 137 |
| abstract_inverted_index.approach | 105 |
| abstract_inverted_index.compared | 201 |
| abstract_inverted_index.enhanced | 142 |
| abstract_inverted_index.exchange | 17 |
| abstract_inverted_index.focusing | 328 |
| abstract_inverted_index.indicate | 286 |
| abstract_inverted_index.involves | 128 |
| abstract_inverted_index.learning | 103, 315 |
| abstract_inverted_index.manifest | 220 |
| abstract_inverted_index.multiple | 204 |
| abstract_inverted_index.network. | 163 |
| abstract_inverted_index.networks | 59 |
| abstract_inverted_index.oceanic, | 14 |
| abstract_inverted_index.physical | 7 |
| abstract_inverted_index.proposed | 298 |
| abstract_inverted_index.retrieve | 252 |
| abstract_inverted_index.superior | 154, 243 |
| abstract_inverted_index.tackling | 66 |
| abstract_inverted_index.training | 158 |
| abstract_inverted_index.valuable | 323 |
| abstract_inverted_index.yielding | 261 |
| abstract_inverted_index.Algorithm | 117 |
| abstract_inverted_index.Employing | 164 |
| abstract_inverted_index.R-squared | 223 |
| abstract_inverted_index.accuracy. | 244 |
| abstract_inverted_index.adeptness | 64 |
| abstract_inverted_index.air–sea | 16 |
| abstract_inverted_index.algorithm | 144 |
| abstract_inverted_index.attesting | 240 |
| abstract_inverted_index.capturing | 49 |
| abstract_inverted_index.conducted | 199 |
| abstract_inverted_index.construct | 179 |
| abstract_inverted_index.employing | 145, 313 |
| abstract_inverted_index.excellent | 282 |
| abstract_inverted_index.interplay | 52 |
| abstract_inverted_index.intricate | 51, 69 |
| abstract_inverted_index.microwave | 26 |
| abstract_inverted_index.mitigates | 30 |
| abstract_inverted_index.nonlinear | 67 |
| abstract_inverted_index.parameter | 8 |
| abstract_inverted_index.potential | 311 |
| abstract_inverted_index.problems, | 70 |
| abstract_inverted_index.reference | 324 |
| abstract_inverted_index.rendering | 88 |
| abstract_inverted_index.retrieval | 20, 108, 319, 331 |
| abstract_inverted_index.retrieved | 294 |
| abstract_inverted_index.satellite | 24, 304 |
| abstract_inverted_index.Leveraging | 57 |
| abstract_inverted_index.accurately | 293 |
| abstract_inverted_index.addressing | 37 |
| abstract_inverted_index.algorithms | 316 |
| abstract_inverted_index.brightness | 166 |
| abstract_inverted_index.challenge. | 40 |
| abstract_inverted_index.functional | 43 |
| abstract_inverted_index.impressive | 222 |
| abstract_inverted_index.parameters | 155 |
| abstract_inverted_index.processes. | 18 |
| abstract_inverted_index.regression | 206 |
| abstract_inverted_index.retrieval. | 76, 187 |
| abstract_inverted_index.technology | 28 |
| abstract_inverted_index.thresholds | 139 |
| abstract_inverted_index.unimproved | 215 |
| abstract_inverted_index.validation | 189 |
| abstract_inverted_index.constitutes | 4 |
| abstract_inverted_index.effectively | 29 |
| abstract_inverted_index.efficiently | 291 |
| abstract_inverted_index.influencing | 55 |
| abstract_inverted_index.network’s | 135 |
| abstract_inverted_index.parameters. | 336 |
| abstract_inverted_index.sensitivity | 82 |
| abstract_inverted_index.strategies. | 148 |
| abstract_inverted_index.susceptible | 90 |
| abstract_inverted_index.temperature | 2, 167, 186 |
| abstract_inverted_index.thresholds, | 87 |
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| abstract_inverted_index.concordance. | 283 |
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| abstract_inverted_index.underscoring | 280 |
| abstract_inverted_index.Nevertheless, | 41 |
| abstract_inverted_index.investigation | 11 |
| abstract_inverted_index.measurements, | 111, 177 |
| abstract_inverted_index.measurements. | 306 |
| abstract_inverted_index.methodologies. | 124 |
| abstract_inverted_index.representations | 44 |
| abstract_inverted_index.Back-Propagation | 120 |
| abstract_inverted_index.root-mean-square | 230 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5101515060 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I200845125 |
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