Next Article in Journal
Developing a POS Tagged Corpus of Urdu Tweets
Previous Article in Journal
Minimal Complexity Support Vector Machines for Pattern Classification
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Computers
Volume 9
Issue 4
10.3390/computers9040089
computers-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

by
Ola M. Surakhi
1,
Martha Arbayani Zaidan
2,3,
Sami Serhan
1,
Imad Salah
1 and
Tareq Hussein
2,4,5,*
1
Department of Computer Science, The University of Jordan, Amman 11942, Jordan
2
Institute for Atmospheric and Earth System Research (INAR/Physics), University of Helsinki, FI-00014 Helsinki, Finland
3
Joint International Research Laboratory of Atmospheric and Earth System Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
4
Department Material Analysis and Indoor Chemistry, Fraunhofer WKI, D-38108 Braunschweig, Germany
5
Department of Physics, The University of Jordan, Amman 11942, Jordan
*
Author to whom correspondence should be addressed.
Computers 2020, 9(4), 89; https://doi.org/10.3390/computers9040089
Submission received: 8 September 2020 / Revised: 30 October 2020 / Accepted: 31 October 2020 / Published: 5 November 2020
Browse Figures
Versions Notes

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.
Keywords: ensemble learning; heuristic algorithm; optimization; recurrent neural network ensemble learning; heuristic algorithm; optimization; recurrent neural network

Share and Cite

MDPI and ACS Style

Surakhi, O.M.; Zaidan, M.A.; Serhan, S.; Salah, I.; Hussein, T. An Optimal Stacked Ensemble Deep Learning Model for Predicting Time-Series Data Using a Genetic Algorithm—An Application for Aerosol Particle Number Concentrations. Computers 2020, 9, 89. https://doi.org/10.3390/computers9040089

AMA Style

Surakhi OM, Zaidan MA, Serhan S, Salah I, Hussein T. An Optimal Stacked Ensemble Deep Learning Model for Predicting Time-Series Data Using a Genetic Algorithm—An Application for Aerosol Particle Number Concentrations. Computers. 2020; 9(4):89. https://doi.org/10.3390/computers9040089

Chicago/Turabian Style

Surakhi, Ola M., Martha Arbayani Zaidan, Sami Serhan, Imad Salah, and Tareq Hussein. 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 9, no. 4: 89. https://doi.org/10.3390/computers9040089

APA Style

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, 9(4), 89. https://doi.org/10.3390/computers9040089

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop