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

dc.creatorBecerra M.A.spa
dc.creatorSánchez M.B.spa
dc.creatorCarvajal J.G.spa
dc.creatorLuna J.A.G.spa
dc.creatorPeluffo-Ordóñez D.H.spa
dc.creatorTobón C.spa
dc.date.accessioned2017-12-19T19:36:44Z
dc.date.available2017-12-19T19:36:44Z
dc.date.created2017
dc.identifier.isbn9783319522760
dc.identifier.issn3029743
dc.identifier.urihttp://hdl.handle.net/11407/4277
dc.description.abstractNowadays, 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.eng
dc.language.isoeng
dc.publisherSpringer Verlagspa
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85013471394&doi=10.1007%2f978-3-319-52277-7_52&partnerID=40&md5=668dac684d7746221537a90a90404d13spa
dc.sourceScopusspa
dc.titleData fusion from multiple stations for estimation of PM2.5 in specific geographical locationspa
dc.typeConference Papereng
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.contributor.affiliationBecerra, M.A., GEA Research Group, Institución Universitaria Salazar y Herrera, Medellín, Colombia, SINTELWEB Research Group, Universidad Nacional de Colombia, Medellín, Colombiaspa
dc.contributor.affiliationSánchez, M.B., GEA Research Group, Institución Universitaria Salazar y Herrera, Medellín, Colombiaspa
dc.contributor.affiliationCarvajal, J.G., GEA Research Group, Institución Universitaria Salazar y Herrera, Medellín, Colombiaspa
dc.contributor.affiliationLuna, J.A.G., SINTELWEB Research Group, Universidad Nacional de Colombia, Medellín, Colombiaspa
dc.contributor.affiliationPeluffo-Ordóñez, D.H., Facultad de Ingeniería en Ciencias Aplicadas-FICA, Universidad Técnica del Norte, Ibarra, Ecuador, Department of Electronics, Universidad de Nariño, Pasto, Colombiaspa
dc.contributor.affiliationTobón, C., Universidad de Medellín, Medellín, Colombiaspa
dc.identifier.doi10.1007/978-3-319-52277-7_52
dc.subject.keywordANFISeng
dc.subject.keywordPM2.5 estimationeng
dc.subject.keywordSupport vector regressioneng
dc.subject.keywordAir qualityeng
dc.subject.keywordData fusioneng
dc.subject.keywordLocationeng
dc.subject.keywordPattern recognitioneng
dc.subject.keywordPublic healtheng
dc.subject.keywordAdaptive neural fuzzy inference system (ANFIS)eng
dc.subject.keywordAir quality networkseng
dc.subject.keywordANFISeng
dc.subject.keywordContamination levelseng
dc.subject.keywordEnvironmental databaseeng
dc.subject.keywordGeographical locationseng
dc.subject.keywordMeteorological variableseng
dc.subject.keywordSupport vector regression (SVR)eng
dc.subject.keywordFuzzy inferenceeng
dc.publisher.facultyFacultad de Ciencias Básicasspa
dc.abstractNowadays, 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.eng
dc.creator.affiliationGEA Research Group, Institución Universitaria Salazar y Herrera, Medellín, Colombiaspa
dc.creator.affiliationSINTELWEB Research Group, Universidad Nacional de Colombia, Medellín, Colombiaspa
dc.creator.affiliationFacultad de Ingeniería en Ciencias Aplicadas-FICA, Universidad Técnica del Norte, Ibarra, Ecuadorspa
dc.creator.affiliationDepartment of Electronics, Universidad de Nariño, Pasto, Colombiaspa
dc.creator.affiliationUniversidad de Medellín, Medellín, Colombiaspa
dc.relation.ispartofesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)spa
dc.relation.ispartofesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 10125 LNCS, 2017, Pages 426-433spa
dc.relation.referencesAntanasijević, 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.2012.10.110spa
dc.relation.referencesBehrang, 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.2010.05.009spa
dc.relation.referencesChiu, S. L. (1994). Fuzzy model identification based on cluster estimation. Journal of Intelligent and Fuzzy Systems, 2(3), 267-278. doi:10.3233/IFS-1994-2306spa
dc.relation.referencesDeo, 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.2016.01.130spa
dc.relation.referencesDong, 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.2008.12.017spa
dc.relation.referencesFeng, 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.2015.02.030spa
dc.relation.referencesGardner, 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)00230-1spa
dc.relation.referencesHrust, 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.2009.07.048spa
dc.relation.referencesJang, J. -. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics, 23(3), 665-685. doi:10.1109/21.256541spa
dc.relation.referencesKumar, 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.2011.08.069spa
dc.relation.referencesLohani, 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.2013.11.021spa
dc.relation.referencesMishra, 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.2014.11.050spa
dc.relation.referencesNoori, 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.2009.11.005spa
dc.relation.referencesOrrego, 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.2012.6347186spa
dc.relation.referencesPai, 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.201000067spa
dc.relation.referencesPerez, 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.2015.11.016spa
dc.relation.referencesPolat, 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.spa
dc.relation.referencesPopoola, 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.2015.08.028spa
dc.relation.referencesQin, 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.2014.09.046spa
dc.relation.referencesSun, 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.2012.10.070spa
dc.relation.referencesVapnik, V. (1995). The Nature of Statistical Learning Theory.spa
dc.relation.referencesVelásquez, 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.spa
dc.relation.referencesYildirim, 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.2005.08.070spa
dc.relation.referencesZhou, 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.2014.07.051spa
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.driverinfo:eu-repo/semantics/conferenceObject
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellínspa
dc.identifier.instnameinstname:Universidad de Medellínspa


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