Data fusion from multiple stations for estimation of PM2.5 in specific geographical location

Nowadays, an important decrease in the quality of the air has been observed, due to the presence of contamination levels that can change the natural composition of the air. This fact represents a problem not only for the environment, but also for the public health. Consequently, this paper presents...

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Fecha de publicación:: 2017

Institución:: Universidad de Medellín

Repositorio:: Repositorio UDEM

Idioma:: eng

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network_acronym_str	REPOUDEM2
network_name_str	Repositorio UDEM
repository_id_str
dc.title.spa.fl_str_mv	Data fusion from multiple stations for estimation of PM2.5 in specific geographical location
title	Data fusion from multiple stations for estimation of PM2.5 in specific geographical location
spellingShingle	Data fusion from multiple stations for estimation of PM2.5 in specific geographical location ANFIS PM2.5 estimation Support vector regression Air quality Data fusion Location Pattern recognition Public health Adaptive neural fuzzy inference system (ANFIS) Air quality networks ANFIS Contamination levels Environmental database Geographical locations Meteorological variables Support vector regression (SVR) Fuzzy inference
title_short	Data fusion from multiple stations for estimation of PM2.5 in specific geographical location
title_full	Data fusion from multiple stations for estimation of PM2.5 in specific geographical location
title_fullStr	Data fusion from multiple stations for estimation of PM2.5 in specific geographical location
title_full_unstemmed	Data fusion from multiple stations for estimation of PM2.5 in specific geographical location
title_sort	Data fusion from multiple stations for estimation of PM2.5 in specific geographical location
dc.contributor.affiliation.spa.fl_str_mv	Becerra, M.A., GEA Research Group, Institución Universitaria Salazar y Herrera, Medellín, Colombia, SINTELWEB Research Group, Universidad Nacional de Colombia, Medellín, Colombia Sánchez, M.B., GEA Research Group, Institución Universitaria Salazar y Herrera, Medellín, Colombia Carvajal, J.G., GEA Research Group, Institución Universitaria Salazar y Herrera, Medellín, Colombia Luna, J.A.G., SINTELWEB Research Group, Universidad Nacional de Colombia, Medellín, Colombia Peluffo-Ordóñez, D.H., Facultad de Ingeniería en Ciencias Aplicadas-FICA, Universidad Técnica del Norte, Ibarra, Ecuador, Department of Electronics, Universidad de Nariño, Pasto, Colombia Tobón, C., Universidad de Medellín, Medellín, Colombia
dc.subject.keyword.eng.fl_str_mv	ANFIS PM2.5 estimation Support vector regression Air quality Data fusion Location Pattern recognition Public health Adaptive neural fuzzy inference system (ANFIS) Air quality networks ANFIS Contamination levels Environmental database Geographical locations Meteorological variables Support vector regression (SVR) Fuzzy inference
topic	ANFIS PM2.5 estimation Support vector regression Air quality Data fusion Location Pattern recognition Public health Adaptive neural fuzzy inference system (ANFIS) Air quality networks ANFIS Contamination levels Environmental database Geographical locations Meteorological variables Support vector regression (SVR) Fuzzy inference
description	Nowadays, an important decrease in the quality of the air has been observed, due to the presence of contamination levels that can change the natural composition of the air. This fact represents a problem not only for the environment, but also for the public health. Consequently, this paper presents a comparison among approaches based on Adaptive Neural Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) for the estimation level of PM2.5 (Particle Material 2.5) in specific geographic locations based on nearby stations. The systems were validated using an environmental database that belongs to air quality network of Valle de Aburrá (AMVA) of Medellin Colombia, which has the registration of 5 meteorological variables and 2 pollutants that are from 3 nearby measurement stations. Therefore, this project analyses the relevance of the characteristics obtained in every single station to estimate the levels of PM2.5 in the target station, using four different selectors based on Rough Set Feature Selection (RSFS) algorithms. Additionally, five systems to estimate the PM2.5 were compared: three based on ANFIS, and two based on SVR to obtain an aim and an efficient mechanism to estimate the levels of PM2.5 in specific geographic locations fusing data obtained from the near monitoring stations. © Springer International Publishing AG 2017.
publishDate	2017
dc.date.accessioned.none.fl_str_mv	2017-12-19T19:36:44Z
dc.date.available.none.fl_str_mv	2017-12-19T19:36:44Z
dc.date.created.none.fl_str_mv	2017
dc.type.eng.fl_str_mv	Conference Paper
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dc.identifier.isbn.none.fl_str_mv	9783319522760
dc.identifier.issn.none.fl_str_mv	3029743
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/11407/4277
dc.identifier.doi.none.fl_str_mv	10.1007/978-3-319-52277-7_52
dc.identifier.reponame.spa.fl_str_mv	reponame:Repositorio Institucional Universidad de Medellín
dc.identifier.instname.spa.fl_str_mv	instname:Universidad de Medellín
identifier_str_mv	9783319522760 3029743 10.1007/978-3-319-52277-7_52 reponame:Repositorio Institucional Universidad de Medellín instname:Universidad de Medellín
url	http://hdl.handle.net/11407/4277
dc.language.iso.none.fl_str_mv	eng
language	eng
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dc.relation.ispartofes.spa.fl_str_mv	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 10125 LNCS, 2017, Pages 426-433
dc.relation.references.spa.fl_str_mv	Antanasijević, D. Z., Pocajt, V. V., Povrenović, D. S., Ristić, M. T., & Perić-Grujić, A. A. (2013). PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Science of the Total Environment, 443, 511-519. doi:10.1016/j.scitotenv.2012.10.110 Behrang, M. A., Assareh, E., Ghanbarzadeh, A., & Noghrehabadi, A. R. (2010). The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data. Solar Energy, 84(8), 1468-1480. doi:10.1016/j.solener.2010.05.009 Chiu, S. L. (1994). Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems, 2(3), 267-278. doi:10.3233/IFS-1994-2306 Deo, R. C., Wen, X., & Qi, F. (2016). A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset. Applied Energy, 168, 568-593. doi:10.1016/j.apenergy.2016.01.130 Dong, M., Yang, D., Kuang, Y., He, D., Erdal, S., & Kenski, D. (2009). PM2.5 concentration prediction using hidden semi-markov model-based times series data mining. Expert Systems with Applications, 36(5), 9046-9055. doi:10.1016/j.eswa.2008.12.017 Feng, X., Li, Q., Zhu, Y., Hou, J., Jin, L., & Wang, J. (2015). Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmospheric Environment, 107, 118-128. doi:10.1016/j.atmosenv.2015.02.030 Gardner, M. W., & Dorling, S. R. (1999). Neural network modelling and prediction of hourly NO(x) and NO2 concentrations in urban air in london. Atmospheric Environment, 33(5), 709-719. doi:10.1016/S1352-2310(98)00230-1 Hrust, L., Klaić, Z. B., Križan, J., Antonić, O., & Hercog, P. (2009). Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations. Atmospheric Environment, 43(35), 5588-5596. doi:10.1016/j.atmosenv.2009.07.048 Jang, J. -. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics, 23(3), 665-685. doi:10.1109/21.256541 Kumar, A., & Goyal, P. (2011). Forecasting of daily air quality index in delhi. Science of the Total Environment, 409(24), 5517-5523. doi:10.1016/j.scitotenv.2011.08.069 Lohani, A. K., Goel, N. K., & Bhatia, K. K. S. (2014). Improving real time flood forecasting using fuzzy inference system. Journal of Hydrology, 509, 25-41. doi:10.1016/j.jhydrol.2013.11.021 Mishra, D., Goyal, P., & Upadhyay, A. (2015). Artificial intelligence based approach to forecast PM2.5 during haze episodes: A case study of delhi, india. Atmospheric Environment, 102, 239-248. doi:10.1016/j.atmosenv.2014.11.050 Noori, R., Hoshyaripour, G., Ashrafi, K., & Araabi, B. N. (2010). Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration. Atmospheric Environment, 44(4), 476-482. doi:10.1016/j.atmosenv.2009.11.005 Orrego, D. A., Becerra, M. A., & Delgado-Trejos, E. (2012). Dimensionality reduction based on fuzzy rough sets oriented to ischemia detection. Paper presented at the Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 5282-5285. doi:10.1109/EMBC.2012.6347186 Pai, T. -., Hanaki, K., Su, H. -., & Yu, L. -. (2013). A 24-h forecast of oxidant concentration in tokyo using neural network and fuzzy learning approach. Clean - Soil, Air, Water, 41(8), 729-736. doi:10.1002/clen.201000067 Perez, P., & Gramsch, E. (2016). Forecasting hourly PM2.5 in santiago de chile with emphasis on night episodes. Atmospheric Environment, 124, 22-27. doi:10.1016/j.atmosenv.2015.11.016 Polat, K. (2001). A novel data preprocessing method to estimate the air pollution (SO2): Neighbor-based feature scaling (NBFS). Neural Comput.Appl, 21(8), 1-8. Popoola, O., Munda, J., Mpanda, A., & Popoola, A. P. I. (2015). Comparative analysis and assessment of ANFIS-based domestic lighting profile modelling. Energy and Buildings, 107, 294-306. doi:10.1016/j.enbuild.2015.08.028 Qin, S., Liu, F., Wang, J., & Sun, B. (2014). Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of china using hybrid models. Atmospheric Environment, 98, 665-675. doi:10.1016/j.atmosenv.2014.09.046 Sun, W., Zhang, H., Palazoglu, A., Singh, A., Zhang, W., & Liu, S. (2013). Prediction of 24-hour-average PM2.5 concentrations using a hidden markov model with different emission distributions in northern california. Science of the Total Environment, 443, 93-103. doi:10.1016/j.scitotenv.2012.10.070 Vapnik, V. (1995). The Nature of Statistical Learning Theory. Velásquez, J. D., Olaya, Y., & Franco, C. J. (2010). Time series prediction using support vector machines. [Predicción de series temporales usando máquinas de vectores de soporte] Ingeniare, 18(1), 64-75. Yildirim, Y., & Bayramoglu, M. (2006). Adaptive neuro-fuzzy based modelling for prediction of air pollution daily levels in city of zonguldak. Chemosphere, 63(9), 1575-1582. doi:10.1016/j.chemosphere.2005.08.070 Zhou, Q., Jiang, H., Wang, J., & Zhou, J. (2014). A hybrid model for PM2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network. Science of the Total Environment, 496, 264-274. doi:10.1016/j.scitotenv.2014.07.051
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_16ec
rights_invalid_str_mv	http://purl.org/coar/access_right/c_16ec
dc.publisher.spa.fl_str_mv	Springer Verlag
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias Básicas
dc.source.spa.fl_str_mv	Scopus
institution	Universidad de Medellín
repository.name.fl_str_mv	Repositorio Institucional Universidad de Medellin
repository.mail.fl_str_mv	repositorio@udem.edu.co
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spelling	2017-12-19T19:36:44Z2017-12-19T19:36:44Z201797833195227603029743http://hdl.handle.net/11407/427710.1007/978-3-319-52277-7_52reponame:Repositorio Institucional Universidad de Medellíninstname:Universidad de MedellínNowadays, an important decrease in the quality of the air has been observed, due to the presence of contamination levels that can change the natural composition of the air. This fact represents a problem not only for the environment, but also for the public health. Consequently, this paper presents a comparison among approaches based on Adaptive Neural Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) for the estimation level of PM2.5 (Particle Material 2.5) in specific geographic locations based on nearby stations. The systems were validated using an environmental database that belongs to air quality network of Valle de Aburrá (AMVA) of Medellin Colombia, which has the registration of 5 meteorological variables and 2 pollutants that are from 3 nearby measurement stations. Therefore, this project analyses the relevance of the characteristics obtained in every single station to estimate the levels of PM2.5 in the target station, using four different selectors based on Rough Set Feature Selection (RSFS) algorithms. Additionally, five systems to estimate the PM2.5 were compared: three based on ANFIS, and two based on SVR to obtain an aim and an efficient mechanism to estimate the levels of PM2.5 in specific geographic locations fusing data obtained from the near monitoring stations. © Springer International Publishing AG 2017.engSpringer VerlagFacultad de Ciencias Básicashttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85013471394&doi=10.1007%2f978-3-319-52277-7_52&partnerID=40&md5=668dac684d7746221537a90a90404d13Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 10125 LNCS, 2017, Pages 426-433Antanasijević, D. Z., Pocajt, V. V., Povrenović, D. S., Ristić, M. T., & Perić-Grujić, A. A. (2013). PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Science of the Total Environment, 443, 511-519. doi:10.1016/j.scitotenv.2012.10.110Behrang, M. A., Assareh, E., Ghanbarzadeh, A., & Noghrehabadi, A. R. (2010). The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data. Solar Energy, 84(8), 1468-1480. doi:10.1016/j.solener.2010.05.009Chiu, S. L. (1994). Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems, 2(3), 267-278. doi:10.3233/IFS-1994-2306Deo, R. C., Wen, X., & Qi, F. (2016). A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset. Applied Energy, 168, 568-593. doi:10.1016/j.apenergy.2016.01.130Dong, M., Yang, D., Kuang, Y., He, D., Erdal, S., & Kenski, D. (2009). PM2.5 concentration prediction using hidden semi-markov model-based times series data mining. Expert Systems with Applications, 36(5), 9046-9055. doi:10.1016/j.eswa.2008.12.017Feng, X., Li, Q., Zhu, Y., Hou, J., Jin, L., & Wang, J. (2015). Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmospheric Environment, 107, 118-128. doi:10.1016/j.atmosenv.2015.02.030Gardner, M. W., & Dorling, S. R. (1999). Neural network modelling and prediction of hourly NO(x) and NO2 concentrations in urban air in london. Atmospheric Environment, 33(5), 709-719. doi:10.1016/S1352-2310(98)00230-1Hrust, L., Klaić, Z. B., Križan, J., Antonić, O., & Hercog, P. (2009). Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations. Atmospheric Environment, 43(35), 5588-5596. doi:10.1016/j.atmosenv.2009.07.048Jang, J. -. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics, 23(3), 665-685. doi:10.1109/21.256541Kumar, A., & Goyal, P. (2011). Forecasting of daily air quality index in delhi. Science of the Total Environment, 409(24), 5517-5523. doi:10.1016/j.scitotenv.2011.08.069Lohani, A. K., Goel, N. K., & Bhatia, K. K. S. (2014). Improving real time flood forecasting using fuzzy inference system. Journal of Hydrology, 509, 25-41. doi:10.1016/j.jhydrol.2013.11.021Mishra, D., Goyal, P., & Upadhyay, A. (2015). Artificial intelligence based approach to forecast PM2.5 during haze episodes: A case study of delhi, india. Atmospheric Environment, 102, 239-248. doi:10.1016/j.atmosenv.2014.11.050Noori, R., Hoshyaripour, G., Ashrafi, K., & Araabi, B. N. (2010). Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration. Atmospheric Environment, 44(4), 476-482. doi:10.1016/j.atmosenv.2009.11.005Orrego, D. A., Becerra, M. A., & Delgado-Trejos, E. (2012). Dimensionality reduction based on fuzzy rough sets oriented to ischemia detection. Paper presented at the Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 5282-5285. doi:10.1109/EMBC.2012.6347186Pai, T. -., Hanaki, K., Su, H. -., & Yu, L. -. (2013). A 24-h forecast of oxidant concentration in tokyo using neural network and fuzzy learning approach. Clean - Soil, Air, Water, 41(8), 729-736. doi:10.1002/clen.201000067Perez, P., & Gramsch, E. (2016). Forecasting hourly PM2.5 in santiago de chile with emphasis on night episodes. Atmospheric Environment, 124, 22-27. doi:10.1016/j.atmosenv.2015.11.016Polat, K. (2001). A novel data preprocessing method to estimate the air pollution (SO2): Neighbor-based feature scaling (NBFS). Neural Comput.Appl, 21(8), 1-8.Popoola, O., Munda, J., Mpanda, A., & Popoola, A. P. I. (2015). Comparative analysis and assessment of ANFIS-based domestic lighting profile modelling. Energy and Buildings, 107, 294-306. doi:10.1016/j.enbuild.2015.08.028Qin, S., Liu, F., Wang, J., & Sun, B. (2014). Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of china using hybrid models. Atmospheric Environment, 98, 665-675. doi:10.1016/j.atmosenv.2014.09.046Sun, W., Zhang, H., Palazoglu, A., Singh, A., Zhang, W., & Liu, S. (2013). Prediction of 24-hour-average PM2.5 concentrations using a hidden markov model with different emission distributions in northern california. Science of the Total Environment, 443, 93-103. doi:10.1016/j.scitotenv.2012.10.070Vapnik, V. (1995). The Nature of Statistical Learning Theory.Velásquez, J. D., Olaya, Y., & Franco, C. J. (2010). Time series prediction using support vector machines. [Predicción de series temporales usando máquinas de vectores de soporte] Ingeniare, 18(1), 64-75.Yildirim, Y., & Bayramoglu, M. (2006). Adaptive neuro-fuzzy based modelling for prediction of air pollution daily levels in city of zonguldak. Chemosphere, 63(9), 1575-1582. doi:10.1016/j.chemosphere.2005.08.070Zhou, Q., Jiang, H., Wang, J., & Zhou, J. (2014). A hybrid model for PM2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network. Science of the Total Environment, 496, 264-274. doi:10.1016/j.scitotenv.2014.07.051ScopusData fusion from multiple stations for estimation of PM2.5 in specific geographical locationConference Paperinfo:eu-repo/semantics/conferenceObjecthttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fBecerra, M.A., GEA Research Group, Institución Universitaria Salazar y Herrera, Medellín, Colombia, SINTELWEB Research Group, Universidad Nacional de Colombia, Medellín, ColombiaSánchez, M.B., GEA Research Group, Institución Universitaria Salazar y Herrera, Medellín, ColombiaCarvajal, J.G., GEA Research Group, Institución Universitaria Salazar y Herrera, Medellín, ColombiaLuna, J.A.G., SINTELWEB Research Group, Universidad Nacional de Colombia, Medellín, ColombiaPeluffo-Ordóñez, D.H., Facultad de Ingeniería en Ciencias Aplicadas-FICA, Universidad Técnica del Norte, Ibarra, Ecuador, Department of Electronics, Universidad de Nariño, Pasto, ColombiaTobón, C., Universidad de Medellín, Medellín, ColombiaBecerra M.A.Sánchez M.B.Carvajal J.G.Luna J.A.G.Peluffo-Ordóñez D.H.Tobón C.GEA Research Group, Institución Universitaria Salazar y Herrera, Medellín, ColombiaSINTELWEB Research Group, Universidad Nacional de Colombia, Medellín, ColombiaFacultad de Ingeniería en Ciencias Aplicadas-FICA, Universidad Técnica del Norte, Ibarra, EcuadorDepartment of Electronics, Universidad de Nariño, Pasto, ColombiaUniversidad de Medellín, Medellín, ColombiaANFISPM2.5 estimationSupport vector regressionAir qualityData fusionLocationPattern recognitionPublic healthAdaptive neural fuzzy inference system (ANFIS)Air quality networksANFISContamination levelsEnvironmental databaseGeographical locationsMeteorological variablesSupport vector regression (SVR)Fuzzy inferenceNowadays, an important decrease in the quality of the air has been observed, due to the presence of contamination levels that can change the natural composition of the air. This fact represents a problem not only for the environment, but also for the public health. Consequently, this paper presents a comparison among approaches based on Adaptive Neural Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) for the estimation level of PM2.5 (Particle Material 2.5) in specific geographic locations based on nearby stations. The systems were validated using an environmental database that belongs to air quality network of Valle de Aburrá (AMVA) of Medellin Colombia, which has the registration of 5 meteorological variables and 2 pollutants that are from 3 nearby measurement stations. Therefore, this project analyses the relevance of the characteristics obtained in every single station to estimate the levels of PM2.5 in the target station, using four different selectors based on Rough Set Feature Selection (RSFS) algorithms. Additionally, five systems to estimate the PM2.5 were compared: three based on ANFIS, and two based on SVR to obtain an aim and an efficient mechanism to estimate the levels of PM2.5 in specific geographic locations fusing data obtained from the near monitoring stations. © Springer International Publishing AG 2017.http://purl.org/coar/access_right/c_16ec11407/4277oai:repository.udem.edu.co:11407/42772020-05-27 17:48:56.191Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co

Data fusion from multiple stations for estimation of PM2.5 in specific geographical location

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