Development, Calibration and Validation of Time Series Analysis and Artificial Neural Network Joint Model for Urban Noise Prediction Article Swipe
Domenico Rossi
,
Daljeet Singh
,
Cláudio Guarnaccia
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14167395
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14167395
Noise in large urban areas, which is mainly generated by road traffic and by the human activities carried out nearby and inside the area under study, is a relevant problem. The continuous exposure to high noise levels, in fact, can lead to several problems, largely documented in the scientific literature. The analysis and forecasting of the noise level in a given area are, then, fundamental for control and prevention, especially when field measurements present peculiar trends and slopes, which can be modeled with a Time Series Analysis approach. In this paper, a hybrid model is presented for the analysis and the forecasting of noise time series in urban areas: this technique is based on the application of a deterministic decomposition model followed in cascade by a predictor of the forecasting errors based on an artificial neural network. Two variants of the hybrid model have been implemented and presented. The time series used to calibrate and validate the model is composed of sound pressure level measurements detected on a busy road near the commercial port of an Italian city. The proposed hybrid model has been calibrated on a part of the entire time series and validated on the remaining part. Residuals and error analysis, together with a detailed statistical description of the simulated noise levels and error metrics describe in detail the method’s performances and its limitations.
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https://doi.org/10.3390/app14167395Digital Object Identifier
- Title
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Development, Calibration and Validation of Time Series Analysis and Artificial Neural Network Joint Model for Urban Noise PredictionWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-08-21Full publication date if available
- Authors
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Domenico Rossi, Daljeet Singh, Cláudio GuarnacciaList of authors in order
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https://doi.org/10.3390/app14167395Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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Artificial neural network, Calibration, Computer science, Series (stratigraphy), Noise (video), Artificial intelligence, Statistics, Mathematics, Geology, Image (mathematics), PaleontologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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30Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.time | 104, 149, 191 |
| abstract_inverted_index.used | 151 |
| abstract_inverted_index.when | 70 |
| abstract_inverted_index.with | 82, 204 |
| abstract_inverted_index.Noise | 0 |
| abstract_inverted_index.based | 112, 131 |
| abstract_inverted_index.city. | 177 |
| abstract_inverted_index.error | 201, 215 |
| abstract_inverted_index.fact, | 38 |
| abstract_inverted_index.field | 71 |
| abstract_inverted_index.given | 60 |
| abstract_inverted_index.human | 15 |
| abstract_inverted_index.large | 2 |
| abstract_inverted_index.level | 57, 163 |
| abstract_inverted_index.model | 93, 120, 142, 157, 181 |
| abstract_inverted_index.noise | 35, 56, 103, 212 |
| abstract_inverted_index.part. | 198 |
| abstract_inverted_index.sound | 161 |
| abstract_inverted_index.then, | 63 |
| abstract_inverted_index.under | 24 |
| abstract_inverted_index.urban | 3, 107 |
| abstract_inverted_index.which | 5, 78 |
| abstract_inverted_index.Series | 85 |
| abstract_inverted_index.areas, | 4 |
| abstract_inverted_index.areas: | 108 |
| abstract_inverted_index.detail | 219 |
| abstract_inverted_index.entire | 190 |
| abstract_inverted_index.errors | 130 |
| abstract_inverted_index.hybrid | 92, 141, 180 |
| abstract_inverted_index.inside | 21 |
| abstract_inverted_index.levels | 213 |
| abstract_inverted_index.mainly | 7 |
| abstract_inverted_index.nearby | 19 |
| abstract_inverted_index.neural | 135 |
| abstract_inverted_index.paper, | 90 |
| abstract_inverted_index.series | 105, 150, 192 |
| abstract_inverted_index.study, | 25 |
| abstract_inverted_index.trends | 75 |
| abstract_inverted_index.Italian | 176 |
| abstract_inverted_index.carried | 17 |
| abstract_inverted_index.cascade | 123 |
| abstract_inverted_index.control | 66 |
| abstract_inverted_index.largely | 44 |
| abstract_inverted_index.levels, | 36 |
| abstract_inverted_index.metrics | 216 |
| abstract_inverted_index.modeled | 81 |
| abstract_inverted_index.present | 73 |
| abstract_inverted_index.several | 42 |
| abstract_inverted_index.slopes, | 77 |
| abstract_inverted_index.traffic | 11 |
| abstract_inverted_index.Analysis | 86 |
| abstract_inverted_index.analysis | 51, 98 |
| abstract_inverted_index.composed | 159 |
| abstract_inverted_index.describe | 217 |
| abstract_inverted_index.detailed | 206 |
| abstract_inverted_index.detected | 165 |
| abstract_inverted_index.exposure | 32 |
| abstract_inverted_index.followed | 121 |
| abstract_inverted_index.network. | 136 |
| abstract_inverted_index.peculiar | 74 |
| abstract_inverted_index.pressure | 162 |
| abstract_inverted_index.problem. | 29 |
| abstract_inverted_index.proposed | 179 |
| abstract_inverted_index.relevant | 28 |
| abstract_inverted_index.together | 203 |
| abstract_inverted_index.validate | 155 |
| abstract_inverted_index.variants | 138 |
| abstract_inverted_index.Residuals | 199 |
| abstract_inverted_index.analysis, | 202 |
| abstract_inverted_index.approach. | 87 |
| abstract_inverted_index.calibrate | 153 |
| abstract_inverted_index.generated | 8 |
| abstract_inverted_index.predictor | 126 |
| abstract_inverted_index.presented | 95 |
| abstract_inverted_index.problems, | 43 |
| abstract_inverted_index.remaining | 197 |
| abstract_inverted_index.simulated | 211 |
| abstract_inverted_index.technique | 110 |
| abstract_inverted_index.validated | 194 |
| abstract_inverted_index.activities | 16 |
| abstract_inverted_index.artificial | 134 |
| abstract_inverted_index.calibrated | 184 |
| abstract_inverted_index.commercial | 172 |
| abstract_inverted_index.continuous | 31 |
| abstract_inverted_index.documented | 45 |
| abstract_inverted_index.especially | 69 |
| abstract_inverted_index.method’s | 221 |
| abstract_inverted_index.presented. | 147 |
| abstract_inverted_index.scientific | 48 |
| abstract_inverted_index.application | 115 |
| abstract_inverted_index.description | 208 |
| abstract_inverted_index.forecasting | 53, 101, 129 |
| abstract_inverted_index.fundamental | 64 |
| abstract_inverted_index.implemented | 145 |
| abstract_inverted_index.literature. | 49 |
| abstract_inverted_index.prevention, | 68 |
| abstract_inverted_index.statistical | 207 |
| abstract_inverted_index.limitations. | 225 |
| abstract_inverted_index.measurements | 72, 164 |
| abstract_inverted_index.performances | 222 |
| abstract_inverted_index.decomposition | 119 |
| abstract_inverted_index.deterministic | 118 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5072951301 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I131729948 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.8399999737739563 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.24116923 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |