An Optimal Stacked Ensemble Deep Learning Model for Predicting Time-Series Data Using a Genetic Algorithm-An Application for Aerosol Particle Number Concentrations

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dc.contributor.author Surakhi, Ola M.
dc.contributor.author Zaidan, Martha Arbayani
dc.contributor.author Serhan, Sami
dc.contributor.author Salah, Imad
dc.contributor.author Hussein, Tareq
dc.date.accessioned 2021-01-27T17:28:02Z
dc.date.available 2021-01-27T17:28:02Z
dc.date.issued 2020-12
dc.identifier.citation Surakhi , O M , Zaidan , M A , Serhan , S , Salah , I & Hussein , T 2020 , ' An Optimal Stacked Ensemble Deep Learning Model for Predicting Time-Series Data Using a Genetic Algorithm-An Application for Aerosol Particle Number Concentrations ' , Computers , vol. 9 , no. 4 , 89 . https://doi.org/10.3390/computers9040089
dc.identifier.other PURE: 159608384
dc.identifier.other PURE UUID: e15094c7-0b72-48da-9a14-fcb1429e5eff
dc.identifier.other WOS: 000601517600001
dc.identifier.other ORCID: /0000-0002-0241-6435/work/87784982
dc.identifier.other ORCID: /0000-0002-6348-1230/work/87788813
dc.identifier.uri http://hdl.handle.net/10138/325340
dc.description.abstract Time-series prediction is an important area that inspires numerous research disciplines for various applications, including air quality databases. Developing a robust and accurate model for time-series data becomes a challenging task, because it involves training different models and optimization. In this paper, we proposed and tested three machine learning techniques—recurrent neural networks (RNN), heuristic algorithm and ensemble learning—to develop a predictive model for estimating atmospheric particle number concentrations in the form of a time-series database. Here, the RNN included three variants—Long-Short Term Memory, Gated Recurrent Network, and Bi-directional Recurrent Neural Network—with various configurations. A Genetic Algorithm (GA) was then used to find the optimal time-lag in order to enhance the model’s performance. The optimized models were used to construct a stacked ensemble model as well as to perform the final prediction. The results demonstrated that the time-lag value can be optimized by using the heuristic algorithm; consequently, this improved the model prediction accuracy. Further improvement can be achieved by using ensemble learning that combines several models for better performance and more accurate predictions. fi
dc.description.abstract Time-series prediction is an important area that inspires numerous research disciplines for various applications, including air quality databases. Developing a robust and accurate model for time-series data becomes a challenging task, because it involves training different models and optimization. In this paper, we proposed and tested three machine learning techniques-recurrent neural networks (RNN), heuristic algorithm and ensemble learning-to develop a predictive model for estimating atmospheric particle number concentrations in the form of a time-series database. Here, the RNN included three variants-Long-Short Term Memory, Gated Recurrent Network, and Bi-directional Recurrent Neural Network-with various configurations. A Genetic Algorithm (GA) was then used to find the optimal time-lag in order to enhance the model's performance. The optimized models were used to construct a stacked ensemble model as well as to perform the final prediction. The results demonstrated that the time-lag value can be optimized by using the heuristic algorithm; consequently, this improved the model prediction accuracy. Further improvement can be achieved by using ensemble learning that combines several models for better performance and more accurate predictions. en
dc.format.extent 26
dc.language.iso eng
dc.relation.ispartof Computers
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 113 Computer and information sciences
dc.subject 112 Statistics and probability
dc.title An Optimal Stacked Ensemble Deep Learning Model for Predicting Time-Series Data Using a Genetic Algorithm-An Application for Aerosol Particle Number Concentrations en
dc.type Article
dc.contributor.organization Institute for Atmospheric and Earth System Research (INAR)
dc.contributor.organization Global Atmosphere-Earth surface feedbacks
dc.contributor.organization Air quality research group
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.3390/computers9040089
dc.relation.issn 2073-431X
dc.rights.accesslevel openAccess
dc.type.version publishedVersion

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