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2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016(1)
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Deep Learning Approach for Assessing Air Quality During COVID-19 Lockdown in Quito
ArticleAbstract: Weather Normalized Models (WNMs) are modeling methods used for assessing air contaminants under a buPalabras claves:air pollution, covid-19, data-driven modeling and optimization, deep learning - artificial neural network (DL-ANN), Machine learningAutores:Chau P.N., Rasa Zalakeviciute, Rybarczyk Y.P., Thomas I.Fuentes:googlescopusGradient boosting machine to assess the public protest impact on urban air quality
ArticleAbstract: Political and economic protests build-up due to the financial uncertainty and inequality spreading tPalabras claves:Machine learning, protests, urban pollutionAutores:DANILO MEJIA CORONEL, Katiuska Alexandrino, Marco G. Bastidas, Rasa Zalakeviciute, Rybarczyk Y.P., Santiago Bonilla-Bedoya, Valeria DíazFuentes:scopusENSEMBLE DEEP LEARNING FOR CLASSIFICATION OF POLLUTION PEAKS
Conference ObjectAbstract: The concentration peaks of atmospheric pollutants are the most challenging and important phenomena iPalabras claves:air pollution forecasting, data-driven modelling, deep learning, Machine learningAutores:Chau P.N., Rasa Zalakeviciute, Rybarczyk Y.P.Fuentes:googlescopusEditorial: Statistical Learning for Pbkp_redicting Air Quality
OtherAbstract:Palabras claves:chemical transport model (CTM), deep learning, Forecast, Machine learning, urban pollutionAutores:Rasa Zalakeviciute, Rybarczyk Y.P.Fuentes:googlescopusMachine learning approach to forecasting urban pollution
Conference ObjectAbstract: This work addresses the question of how to pbkp_redict fine particulate matter given a combination oPalabras claves:decision tree, fine particulate matter, Machine learning, Pbkp_redictive model, urban pollutionAutores:Rasa Zalakeviciute, Rybarczyk Y.P.Fuentes:googlescopus