An Adaptive Kalman Filtering Approach to Sensing and Predicting Air Quality Index Values
In recent years, Air Quality Index (AQI) have been widely used to describe the severity of haze and other air pollutions yet suffers from inefficiency and compatibility on real-time perception and prediction. In this paper, an Auto-Regressive (AR) prediction model based on sensed AQI values is propo...
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| Veröffentlicht in: | IEEE access 2020, Vol.8, p.4265-4272 |
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| description | In recent years, Air Quality Index (AQI) have been widely used to describe the severity of haze and other air pollutions yet suffers from inefficiency and compatibility on real-time perception and prediction. In this paper, an Auto-Regressive (AR) prediction model based on sensed AQI values is proposed, where an adaptive Kalman Filtering (KF) approach is fitted to achieve efficient prediction of the AQI values. The AQI values were collected monthly from January 2018 to March 2019 using a WSN-based network, whereas daily AQI values started to be collected from October 1, 2018 to March 31, 2019. These data have been used for creation and evaluation purposes on the prediction model. According to the results, predicted values have shown high accuracy compared with the actual sensed values. In addition, when monthly AQI values were used, it has depicted higher accuracy compared to the daily ones depending on the experimental results. Therefore, the hybrid AR-KF model is accurate and effective in predicting haze weather, which has practical significance and potential value. |
| doi_str_mv | 10.1109/ACCESS.2019.2963416 |
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In this paper, an Auto-Regressive (AR) prediction model based on sensed AQI values is proposed, where an adaptive Kalman Filtering (KF) approach is fitted to achieve efficient prediction of the AQI values. The AQI values were collected monthly from January 2018 to March 2019 using a WSN-based network, whereas daily AQI values started to be collected from October 1, 2018 to March 31, 2019. These data have been used for creation and evaluation purposes on the prediction model. According to the results, predicted values have shown high accuracy compared with the actual sensed values. In addition, when monthly AQI values were used, it has depicted higher accuracy compared to the daily ones depending on the experimental results. Therefore, the hybrid AR-KF model is accurate and effective in predicting haze weather, which has practical significance and potential value.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2019.2963416</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>PISCATAWAY: IEEE</publisher><subject>Adaptation models ; Adaptive filters ; Air quality ; air quality index ; Atmospheric modeling ; Autoregressive models ; Computer Science ; Computer Science, Information Systems ; Engineering ; Engineering, Electrical & Electronic ; Forecasting ; Haze ; Kalman filter ; Kalman filters ; Mathematical model ; Outdoor air quality ; Prediction models ; Predictions ; Predictive models ; Real-time sensing and predicting ; Science & Technology ; simulation ; Technology ; Telecommunications ; Weather ; Wireless sensor networks</subject><ispartof>IEEE access, 2020, Vol.8, p.4265-4272</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>15</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000545980400001</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c408t-d0718d3a8859c051ed2b5827fd24589b5d68d2efd8b5f1d7b6201835577afa593</citedby><cites>FETCH-LOGICAL-c408t-d0718d3a8859c051ed2b5827fd24589b5d68d2efd8b5f1d7b6201835577afa593</cites><orcidid>0000-0003-3781-6946</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8947921$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>315,781,785,865,2103,2115,4025,27638,27928,27929,27930,28253,54938</link.rule.ids></links><search><creatorcontrib>Chen, Jibo</creatorcontrib><creatorcontrib>Chen, Keyao</creatorcontrib><creatorcontrib>Ding, Chen</creatorcontrib><creatorcontrib>Wang, Guizhi</creatorcontrib><creatorcontrib>Liu, Qi</creatorcontrib><creatorcontrib>Liu, Xiaodong</creatorcontrib><title>An Adaptive Kalman Filtering Approach to Sensing and Predicting Air Quality Index Values</title><title>IEEE access</title><addtitle>Access</addtitle><addtitle>IEEE ACCESS</addtitle><description>In recent years, Air Quality Index (AQI) have been widely used to describe the severity of haze and other air pollutions yet suffers from inefficiency and compatibility on real-time perception and prediction. In this paper, an Auto-Regressive (AR) prediction model based on sensed AQI values is proposed, where an adaptive Kalman Filtering (KF) approach is fitted to achieve efficient prediction of the AQI values. The AQI values were collected monthly from January 2018 to March 2019 using a WSN-based network, whereas daily AQI values started to be collected from October 1, 2018 to March 31, 2019. These data have been used for creation and evaluation purposes on the prediction model. According to the results, predicted values have shown high accuracy compared with the actual sensed values. In addition, when monthly AQI values were used, it has depicted higher accuracy compared to the daily ones depending on the experimental results. Therefore, the hybrid AR-KF model is accurate and effective in predicting haze weather, which has practical significance and potential value.</description><subject>Adaptation models</subject><subject>Adaptive filters</subject><subject>Air quality</subject><subject>air quality index</subject><subject>Atmospheric modeling</subject><subject>Autoregressive models</subject><subject>Computer Science</subject><subject>Computer Science, Information Systems</subject><subject>Engineering</subject><subject>Engineering, Electrical & Electronic</subject><subject>Forecasting</subject><subject>Haze</subject><subject>Kalman filter</subject><subject>Kalman filters</subject><subject>Mathematical model</subject><subject>Outdoor air quality</subject><subject>Prediction models</subject><subject>Predictions</subject><subject>Predictive models</subject><subject>Real-time sensing and predicting</subject><subject>Science & Technology</subject><subject>simulation</subject><subject>Technology</subject><subject>Telecommunications</subject><subject>Weather</subject><subject>Wireless sensor networks</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>AOWDO</sourceid><sourceid>DOA</sourceid><recordid>eNqNkc1u3CAUha2qlRqleYJskLqsZgoGDCwtK2lHidRG00TdIQzXKSMHpphJm7cPjqO0y7ABHZ3v_nCq6pTgNSFYfW677my7XdeYqHWtGspI86Y6qkmjVpTT5u1_7_fVyTTtcDmySFwcVT_bgFpn9tnfA7ow450J6NyPGZIPt6jd71M09hfKEW0hTLNmgkPfEzhv85PFJ3R1MKPPD2gTHPxFN2Y8wPShejeYcYKT5_u4uj4_-9F9XV1--7Lp2suVZVjmlcOCSEeNlFxZzAm4uueyFoOrGZeq566RrobByZ4PxIm-KXtKyrkQZjBc0eNqs9R10ez0Pvk7kx50NF4_CTHdapOytyPoQuOGUGt7wAxAqfIPhCnFHFUDYFpqfVxqla1_lx2y3sVDCmV8XaZhUmAhZxddXDbFaUowvHQlWM-J6CURPSeinxMp1KeF-gN9HCbrIVh4IcsknHElMZvDIcUtX-_ufDbZx9DFQ8gFPV1QD_APkYoJVRP6CFqZpiA</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Chen, Jibo</creator><creator>Chen, Keyao</creator><creator>Ding, Chen</creator><creator>Wang, Guizhi</creator><creator>Liu, Qi</creator><creator>Liu, Xiaodong</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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In this paper, an Auto-Regressive (AR) prediction model based on sensed AQI values is proposed, where an adaptive Kalman Filtering (KF) approach is fitted to achieve efficient prediction of the AQI values. The AQI values were collected monthly from January 2018 to March 2019 using a WSN-based network, whereas daily AQI values started to be collected from October 1, 2018 to March 31, 2019. These data have been used for creation and evaluation purposes on the prediction model. According to the results, predicted values have shown high accuracy compared with the actual sensed values. In addition, when monthly AQI values were used, it has depicted higher accuracy compared to the daily ones depending on the experimental results. Therefore, the hybrid AR-KF model is accurate and effective in predicting haze weather, which has practical significance and potential value.</abstract><cop>PISCATAWAY</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2019.2963416</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0003-3781-6946</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adaptation models Adaptive filters Air quality air quality index Atmospheric modeling Autoregressive models Computer Science Computer Science, Information Systems Engineering Engineering, Electrical & Electronic Forecasting Haze Kalman filter Kalman filters Mathematical model Outdoor air quality Prediction models Predictions Predictive models Real-time sensing and predicting Science & Technology simulation Technology Telecommunications Weather Wireless sensor networks |
| title | An Adaptive Kalman Filtering Approach to Sensing and Predicting Air Quality Index Values |
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