Short-Term Air Quality Prediction Based on Fractional Grey Linear Regression and Support Vector Machine
To predict the daily air pollutants, the fractional multivariable model is established. The hybrid model of the grey multivariable regression model with fractional order accumulation model (FGM(0, m)) and support vector regression model (SVR) is used to predict the air pollutants (PM10, PM2.5, and N...
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| Veröffentlicht in: | Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-13 |
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| creator | Wu, Lifeng Chen, Yan Xu, Zhicun Dun, Meng |
| description | To predict the daily air pollutants, the fractional multivariable model is established. The hybrid model of the grey multivariable regression model with fractional order accumulation model (FGM(0, m)) and support vector regression model (SVR) is used to predict the air pollutants (PM10, PM2.5, and NO2) from December 31, 2018, to January 3, 2019, in Shijiazhuang and Chongqing. The absolute percentage errors (APEs) are used to determine the weights of the FGM(0, m) and SVR. Meanwhile, the Holt–Winters model is used to predict the air quality pollutants for the same location and period. When the mean absolute percent error (MAPE) is 0%–20%, it indicates that the model has good accuracy of fitting and prediction. The MAPE of the hybrid model is less than 20%. It is shown that except for the PM2.5 concentration prediction in Shijiazhuang (13.7%), the MAPE between the forecasting and actual values of the three air pollutants in Shijiazhuang and Chongqing was less than 10%. |
| doi_str_mv | 10.1155/2020/8914501 |
| format | Article |
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The hybrid model of the grey multivariable regression model with fractional order accumulation model (FGM(0, m)) and support vector regression model (SVR) is used to predict the air pollutants (PM10, PM2.5, and NO2) from December 31, 2018, to January 3, 2019, in Shijiazhuang and Chongqing. The absolute percentage errors (APEs) are used to determine the weights of the FGM(0, m) and SVR. Meanwhile, the Holt–Winters model is used to predict the air quality pollutants for the same location and period. When the mean absolute percent error (MAPE) is 0%–20%, it indicates that the model has good accuracy of fitting and prediction. The MAPE of the hybrid model is less than 20%. It is shown that except for the PM2.5 concentration prediction in Shijiazhuang (13.7%), the MAPE between the forecasting and actual values of the three air pollutants in Shijiazhuang and Chongqing was less than 10%.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2020/8914501</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Air pollution ; Air quality ; Algorithms ; Artificial intelligence ; Cities ; Environmental protection ; Model accuracy ; Neural networks ; Nitrogen dioxide ; Outdoor air quality ; Pollutants ; Principal components analysis ; Regression models ; Short term ; Support vector machines</subject><ispartof>Mathematical problems in engineering, 2020, Vol.2020 (2020), p.1-13</ispartof><rights>Copyright © 2020 Meng Dun et al.</rights><rights>Copyright © 2020 Meng Dun et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c360t-81948621f0924d7b81f251c7c4318926c9fcc01363b268186b5deb1ba1d25ab33</citedby><cites>FETCH-LOGICAL-c360t-81948621f0924d7b81f251c7c4318926c9fcc01363b268186b5deb1ba1d25ab33</cites><orcidid>0000-0003-3986-2583 ; 0000-0002-9548-9747</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,4024,27923,27924,27925</link.rule.ids></links><search><contributor>Teodoro, Ana C.</contributor><contributor>Ana C Teodoro</contributor><creatorcontrib>Wu, Lifeng</creatorcontrib><creatorcontrib>Chen, Yan</creatorcontrib><creatorcontrib>Xu, Zhicun</creatorcontrib><creatorcontrib>Dun, Meng</creatorcontrib><title>Short-Term Air Quality Prediction Based on Fractional Grey Linear Regression and Support Vector Machine</title><title>Mathematical problems in engineering</title><description>To predict the daily air pollutants, the fractional multivariable model is established. The hybrid model of the grey multivariable regression model with fractional order accumulation model (FGM(0, m)) and support vector regression model (SVR) is used to predict the air pollutants (PM10, PM2.5, and NO2) from December 31, 2018, to January 3, 2019, in Shijiazhuang and Chongqing. The absolute percentage errors (APEs) are used to determine the weights of the FGM(0, m) and SVR. Meanwhile, the Holt–Winters model is used to predict the air quality pollutants for the same location and period. When the mean absolute percent error (MAPE) is 0%–20%, it indicates that the model has good accuracy of fitting and prediction. The MAPE of the hybrid model is less than 20%. It is shown that except for the PM2.5 concentration prediction in Shijiazhuang (13.7%), the MAPE between the forecasting and actual values of the three air pollutants in Shijiazhuang and Chongqing was less than 10%.</description><subject>Air pollution</subject><subject>Air quality</subject><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Cities</subject><subject>Environmental protection</subject><subject>Model accuracy</subject><subject>Neural networks</subject><subject>Nitrogen dioxide</subject><subject>Outdoor air quality</subject><subject>Pollutants</subject><subject>Principal components analysis</subject><subject>Regression models</subject><subject>Short term</subject><subject>Support vector machines</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqF0E1Lw0AQBuBFFKzVm2dZ8KjRnf1INsdabBUqfrSKt7DZbNotbRN3E6T_3q0pePQ0w_AwzLwInQO5ARDilhJKbmUKXBA4QD0QMYsE8OQw9ITyCCj7PEYn3i8JoSBA9tB8uqhcE82MW-OBdfi1VSvbbPGLM4XVja02-E55U-DQjJz6nagVHjuzxRO7McrhNzN3xvsdVZsCT9u6Divxh9FN5fCT0ovgTtFRqVbenO1rH72P7mfDh2jyPH4cDiaRZjFpIgkplzGFkqSUF0kuoaQCdKI5A5nSWKel1gRYzHIaS5BxLgqTQ66goELljPXRZbe3dtVXa3yTLavWhZN9RjlJUsmByKCuO6Vd5b0zZVY7u1ZumwHJdlFmuyizfZSBX3U8fFKob_ufvui0CcaU6k9TAlIm7AewRnv4</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Wu, Lifeng</creator><creator>Chen, Yan</creator><creator>Xu, Zhicun</creator><creator>Dun, Meng</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0003-3986-2583</orcidid><orcidid>https://orcid.org/0000-0002-9548-9747</orcidid></search><sort><creationdate>2020</creationdate><title>Short-Term Air Quality Prediction Based on Fractional Grey Linear Regression and Support Vector Machine</title><author>Wu, Lifeng ; 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The hybrid model of the grey multivariable regression model with fractional order accumulation model (FGM(0, m)) and support vector regression model (SVR) is used to predict the air pollutants (PM10, PM2.5, and NO2) from December 31, 2018, to January 3, 2019, in Shijiazhuang and Chongqing. The absolute percentage errors (APEs) are used to determine the weights of the FGM(0, m) and SVR. Meanwhile, the Holt–Winters model is used to predict the air quality pollutants for the same location and period. When the mean absolute percent error (MAPE) is 0%–20%, it indicates that the model has good accuracy of fitting and prediction. The MAPE of the hybrid model is less than 20%. 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| subjects | Air pollution Air quality Algorithms Artificial intelligence Cities Environmental protection Model accuracy Neural networks Nitrogen dioxide Outdoor air quality Pollutants Principal components analysis Regression models Short term Support vector machines |
| title | Short-Term Air Quality Prediction Based on Fractional Grey Linear Regression and Support Vector Machine |
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