International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 10, Issue 4 (April 2023), Pages: 76-87

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 Original Research Paper

 Time series forecasting of solid waste generation in selected states in Malaysia

 Author(s): 

 Noryanti Nasir 1, 2, *, S. Sarifah Radiah Shariff 1, 2, 3, Siti Sarah Januri 3, Faridah Zulkipli 4, Zaitul Anna Melisa Md Yasin 5

 Affiliation(s):

 1School of Mathematical Sciences, College of Computing, Informatics, and Media, Universiti Teknologi MARA, Shah Alam, 40450, Selangor, Malaysia
 2Logistic Modelling Research Group, College of Computing, Informatics, and Media Sciences, Universiti Teknologi MARA, Shah Alam, 40450, Selangor, Malaysia
 3Malaysia Institute of Transport (MITRANS), Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
 4Mathematical Sciences Studies, College of Computing, Informatics, and Media, Universiti Teknologi MARA, Negeri Sembilan Branch, Seremban Campus, 70300 Seremban, Negeri Sembilan, Malaysia
 5Mathematical Sciences Studies, College of Computing, Informatics, and Media Sciences, Universiti Teknologi MARA, Perak Branch, Tapah Campus, 35400, Tapah Road, Perak, Malaysia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-1475-0286

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2023.04.009

 Abstract:

This study aims to forecast Malaysian solid waste generation by identifying the state's landfill capacity to facilitate solid waste generated in the next two years. The solid waste management system depends extremely on landfill capacity. Due to the increased amount of solid waste generation, the authority is required to manage landfill utilization appropriately in selected regions, where landfill capacity was fully utilized. An accurate prediction of solid waste generation is required for the authority plan for landfill management. This paper provides the forecasting values for the seven states in Malaysia. The ARMA and ARIMA models are used to determine the best model for forecasting solid waste generation values. The results show that the ARIMA (2, 1, 1) model works best in Johor, Negeri Sembilan, and Wilayah Persekutuan Kuala Lumpur, while the ARIMA (1, 1, 2) model works best in Kedah and Perlis. Furthermore, the ARMA (1, 1) model is best for Pahang, and the ARMA (2, 1) model is best for Melaka. The ARIMA (3, 1, 1) model is the best for forecasting solid waste generation across all states. The findings are consistent with previous literature, which stated that solid waste generation would increase in one of Malaysia's districts over the next two years. They did not, however, consider the landfill's capacity to handle solid waste generation. These findings shed light on the potential volume of solid waste generated in the coming years, allowing authorized agencies to plan landfill capacity in Malaysia for environmental sustainability.

 © 2023 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Solid waste management, Landfills, Forecasting, ARIMA

 Article History: Received 9 September 2022, Received in revised form 30 December 2022, Accepted 6 January 2023

 Acknowledgment 

This research was supported by Universiti Teknologi MARA (UiTM) and funded under UiTM internal. Grant no. 600-RMC/GPM LPHD 5/3 (064/2021). This support is gratefully acknowledged. The authors would like to thank other researchers, lecturers, and friends for their ideas and discussion. A special appreciation to the College of Computing, Informatics, and Media, Universiti Teknologi MARA for supporting the publication of this paper.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Nasir N, Shariff SSR, Januri SS, Zulkipli F, and Yasin ZAMM (2023). Time series forecasting of solid waste generation in selected states in Malaysia. International Journal of Advanced and Applied Sciences, 10(4): 76-87

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4

 Tables

 Table 1 Table 2 Table 3 Table 4 

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