IJESD 2023 Vol.14(4): 245-251
doi: 10.18178/ijesd.2023.14.4.1440
doi: 10.18178/ijesd.2023.14.4.1440
Developing a Hybrid Regression-Metaheuristic Forecasting Model for University Solid Waste Generation
Pongchanun Luangpaiboon and Lakkana Ruekkasaem*
Manuscript received July 31, 2022; revised September 2, 2022; accepted September 8, 2022.
Abstract—The purpose of this study is to investigate and compare forecasting trends of university solid waste (USW) at a private university in Bangkok using a combination of statistical and metaheuristic algorithms. The university’s municipality solid waste data was prepared, collected, and converted so that it could be processed by a decision support system. Historical data is available for 16 years beginning with fiscal year 2005. Factors influencing USW quantities include the number of students, staffs, and others. USW is divided into two data sets: learning data sets and test data sets. The first will be examined using multiple regression and metaheuristics components. The learning datasets differ in data density because factor data is collected on an annual basis. As for the USW data, the data is recorded through the decision support system on a monthly basis. The second will be combined with the proposed method to determine the trend in substituting the prediction equation for USW management. The primary goal of this paper is to develop an effective USW forecasting model to deal with this problem by combining regression and metaheuristic techniques. Based on an empirical analysis of the indicators’ annual data and the USW’s monthly data from 2005 to 2020, we find that the hybrid method based on the linear equation performs well for all performance measures of the mean absolute percentage error (MAPE), mean absolute deviation (MAD), and mean square error (MSE). These findings may be useful in the preparation, configuration, and implementation of a waste management system at a university.
Index Terms—Multiple regression, linear and quadratic equations, metaheuristics, differential evolution
P. Luangpaiboon is with Thammasat University Research Unit in Industrial Statistics and Operational Research, Department of Industrial Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, Thailand.
L. Ruekkasaem is with Faculty of Industrial Technology, Phranakorn Rajabhat University, Bangkok, Thailand.
*Correspondence: lakkana@pnru.ac.th (L.R.)
Index Terms—Multiple regression, linear and quadratic equations, metaheuristics, differential evolution
P. Luangpaiboon is with Thammasat University Research Unit in Industrial Statistics and Operational Research, Department of Industrial Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, Thailand.
L. Ruekkasaem is with Faculty of Industrial Technology, Phranakorn Rajabhat University, Bangkok, Thailand.
*Correspondence: lakkana@pnru.ac.th (L.R.)
Cite: Pongchanun Luangpaiboon and Lakkana Ruekkasaem*, "Developing a Hybrid Regression-Metaheuristic Forecasting Model for University Solid Waste Generation," International Journal of Environmental Science and Development vol. 14, no. 4, pp. 245-251, 2023.
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).General Information
- ISSN: 2010-0264 (Print); 2972-3698 (Online)
- Abbreviated Title: Int. J. Environ. Sci. Dev.
- Frequency: Bimonthly
- DOI: 10.18178/IJESD
- Editor-in-Chief: Prof. Richard Haynes
- Managing Editor: Ms. Cherry L. Chen
- Indexing: Scopus (CiteScore 2022: 1.4), Google Scholar, CNKI, ProQuest, EBSCO, etc.
- E-mail: ijesd@ejournal.net
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