Air Quality Prediction Using the Fractional Gradient-Based Recurrent Neural Network
In this study, the air quality index (AQI) of Indian cities of different tiers is predicted by using the vanilla recurrent neural network (RNN). AQI is used to measure the air quality of any region which is calculated on the basis of the concentration of ground-level ozone, particle pollution, carbo...
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description | In this study, the air quality index (AQI) of Indian cities of different tiers is predicted by using the vanilla recurrent neural network (RNN). AQI is used to measure the air quality of any region which is calculated on the basis of the concentration of ground-level ozone, particle pollution, carbon monoxide, and sulphur dioxide in air. Thus, the present air quality of an area is dependent on current weather conditions, vehicle traffic in that area, or anything that increases air pollution. Also, the current air quality is dependent on the climate conditions and industrialization in that area. Thus, the AQI is history-dependent. To capture this dependency, the memory property of fractional derivatives is exploited in this algorithm and the fractional gradient descent algorithm involving Caputo’s derivative has been used in the backpropagation algorithm for training of the RNN. Due to the availability of a large amount of data and high computation support, deep neural networks are capable of giving state-of-the-art results in the time series prediction. But, in this study, the basic vanilla RNN has been chosen to check the effectiveness of fractional derivatives. The AQI and gases affecting AQI prediction results for different cities show that the proposed algorithm leads to higher accuracy. It has been observed that the results of the vanilla RNN with fractional derivatives are comparable to long short-term memory (LSTM). |
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AQI is used to measure the air quality of any region which is calculated on the basis of the concentration of ground-level ozone, particle pollution, carbon monoxide, and sulphur dioxide in air. Thus, the present air quality of an area is dependent on current weather conditions, vehicle traffic in that area, or anything that increases air pollution. Also, the current air quality is dependent on the climate conditions and industrialization in that area. Thus, the AQI is history-dependent. To capture this dependency, the memory property of fractional derivatives is exploited in this algorithm and the fractional gradient descent algorithm involving Caputo’s derivative has been used in the backpropagation algorithm for training of the RNN. Due to the availability of a large amount of data and high computation support, deep neural networks are capable of giving state-of-the-art results in the time series prediction. But, in this study, the basic vanilla RNN has been chosen to check the effectiveness of fractional derivatives. The AQI and gases affecting AQI prediction results for different cities show that the proposed algorithm leads to higher accuracy. It has been observed that the results of the vanilla RNN with fractional derivatives are comparable to long short-term memory (LSTM).</description><identifier>ISSN: 1687-5265</identifier><identifier>ISSN: 1687-5273</identifier><identifier>EISSN: 1687-5273</identifier><identifier>DOI: 10.1155/2022/9755422</identifier><identifier>PMID: 36531923</identifier><language>eng</language><publisher>United States: Hindawi</publisher><subject>Air Pollutants - analysis ; Air pollution ; Air Pollution - analysis ; Air quality ; Air quality indexes ; Air quality measurements ; Algorithms ; Artificial neural networks ; Back propagation ; Back propagation networks ; Carbon monoxide ; Cities ; Climate change ; Environmental Monitoring - methods ; Forecasting ; Forecasting techniques ; Humidity ; Industrial development ; Integrals ; Long short-term memory ; Neural networks ; Neural Networks, Computer ; Nitrogen dioxide ; Outdoor air quality ; Particulate Matter - analysis ; Pollutants ; Predictions ; Recurrent neural networks ; Statistical methods ; Sulfur ; Sulfur dioxide ; Vanilla ; VOCs ; Volatile organic compounds ; Weather</subject><ispartof>Computational intelligence and neuroscience, 2022-12, Vol.2022, p.9755422-14</ispartof><rights>Copyright © 2022 Sugandha Arora et al.</rights><rights>COPYRIGHT 2022 John Wiley & Sons, Inc.</rights><rights>Copyright © 2022 Sugandha Arora 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. https://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2022 Sugandha Arora et al. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3212-657adef36cf2847c41d0e2517599607233223d10257e2faf4482ed329a3ce9c23</citedby><cites>FETCH-LOGICAL-c3212-657adef36cf2847c41d0e2517599607233223d10257e2faf4482ed329a3ce9c23</cites><orcidid>0000-0001-7067-5239 ; 0000-0002-8866-9192 ; 0000-0001-6050-7274 ; 0000-0002-6606-5152 ; 0000-0002-9595-2009 ; 0000-0001-8116-4987 ; 0000-0002-0512-3179</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9757944/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9757944/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36531923$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Haber, Rodolfo E.</contributor><creatorcontrib>Arora, Sugandha</creatorcontrib><creatorcontrib>Sawaran Singh, Narinderjit Singh</creatorcontrib><creatorcontrib>Singh, Divyanshu</creatorcontrib><creatorcontrib>Rakesh Shrivastava, Rishi</creatorcontrib><creatorcontrib>Mathur, Trilok</creatorcontrib><creatorcontrib>Tiwari, Kamlesh</creatorcontrib><creatorcontrib>Agarwal, Shivi</creatorcontrib><title>Air Quality Prediction Using the Fractional Gradient-Based Recurrent Neural Network</title><title>Computational intelligence and neuroscience</title><addtitle>Comput Intell Neurosci</addtitle><description>In this study, the air quality index (AQI) of Indian cities of different tiers is predicted by using the vanilla recurrent neural network (RNN). AQI is used to measure the air quality of any region which is calculated on the basis of the concentration of ground-level ozone, particle pollution, carbon monoxide, and sulphur dioxide in air. Thus, the present air quality of an area is dependent on current weather conditions, vehicle traffic in that area, or anything that increases air pollution. Also, the current air quality is dependent on the climate conditions and industrialization in that area. Thus, the AQI is history-dependent. To capture this dependency, the memory property of fractional derivatives is exploited in this algorithm and the fractional gradient descent algorithm involving Caputo’s derivative has been used in the backpropagation algorithm for training of the RNN. Due to the availability of a large amount of data and high computation support, deep neural networks are capable of giving state-of-the-art results in the time series prediction. 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It has been observed that the results of the vanilla RNN with fractional derivatives are comparable to long short-term memory (LSTM).</description><subject>Air Pollutants - analysis</subject><subject>Air pollution</subject><subject>Air Pollution - analysis</subject><subject>Air quality</subject><subject>Air quality indexes</subject><subject>Air quality measurements</subject><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Back propagation</subject><subject>Back propagation networks</subject><subject>Carbon monoxide</subject><subject>Cities</subject><subject>Climate change</subject><subject>Environmental Monitoring - methods</subject><subject>Forecasting</subject><subject>Forecasting techniques</subject><subject>Humidity</subject><subject>Industrial development</subject><subject>Integrals</subject><subject>Long short-term memory</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>Nitrogen 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Quality Prediction Using the Fractional Gradient-Based Recurrent Neural Network</title><author>Arora, Sugandha ; Sawaran Singh, Narinderjit Singh ; Singh, Divyanshu ; Rakesh Shrivastava, Rishi ; Mathur, Trilok ; Tiwari, Kamlesh ; Agarwal, Shivi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3212-657adef36cf2847c41d0e2517599607233223d10257e2faf4482ed329a3ce9c23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Air Pollutants - analysis</topic><topic>Air pollution</topic><topic>Air Pollution - analysis</topic><topic>Air quality</topic><topic>Air quality indexes</topic><topic>Air quality measurements</topic><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Back propagation</topic><topic>Back propagation networks</topic><topic>Carbon monoxide</topic><topic>Cities</topic><topic>Climate change</topic><topic>Environmental Monitoring - 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AQI is used to measure the air quality of any region which is calculated on the basis of the concentration of ground-level ozone, particle pollution, carbon monoxide, and sulphur dioxide in air. Thus, the present air quality of an area is dependent on current weather conditions, vehicle traffic in that area, or anything that increases air pollution. Also, the current air quality is dependent on the climate conditions and industrialization in that area. Thus, the AQI is history-dependent. To capture this dependency, the memory property of fractional derivatives is exploited in this algorithm and the fractional gradient descent algorithm involving Caputo’s derivative has been used in the backpropagation algorithm for training of the RNN. Due to the availability of a large amount of data and high computation support, deep neural networks are capable of giving state-of-the-art results in the time series prediction. But, in this study, the basic vanilla RNN has been chosen to check the effectiveness of fractional derivatives. The AQI and gases affecting AQI prediction results for different cities show that the proposed algorithm leads to higher accuracy. It has been observed that the results of the vanilla RNN with fractional derivatives are comparable to long short-term memory (LSTM).</abstract><cop>United States</cop><pub>Hindawi</pub><pmid>36531923</pmid><doi>10.1155/2022/9755422</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0001-7067-5239</orcidid><orcidid>https://orcid.org/0000-0002-8866-9192</orcidid><orcidid>https://orcid.org/0000-0001-6050-7274</orcidid><orcidid>https://orcid.org/0000-0002-6606-5152</orcidid><orcidid>https://orcid.org/0000-0002-9595-2009</orcidid><orcidid>https://orcid.org/0000-0001-8116-4987</orcidid><orcidid>https://orcid.org/0000-0002-0512-3179</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Air Pollutants - analysis Air pollution Air Pollution - analysis Air quality Air quality indexes Air quality measurements Algorithms Artificial neural networks Back propagation Back propagation networks Carbon monoxide Cities Climate change Environmental Monitoring - methods Forecasting Forecasting techniques Humidity Industrial development Integrals Long short-term memory Neural networks Neural Networks, Computer Nitrogen dioxide Outdoor air quality Particulate Matter - analysis Pollutants Predictions Recurrent neural networks Statistical methods Sulfur Sulfur dioxide Vanilla VOCs Volatile organic compounds Weather |
title | Air Quality Prediction Using the Fractional Gradient-Based Recurrent Neural Network |
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