International Journal of


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

Frequency: 12

line decor
line decor

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


 Original Research Paper

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


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


 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

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile:

 Digital Object Identifier:


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 (

 Keywords: Solid waste management, Landfills, Forecasting, ARIMA

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


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.


 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


 Fig. 1 Fig. 2 Fig. 3 Fig. 4


 Table 1 Table 2 Table 3 Table 4 


 References (22)

  1. Arzo A, Naznin S, and Moloy MDJ (2021). Modeling and forecasting of time series data using different techniques. Journal Multicultural Education, 7(11): 474-482.   [Google Scholar]
  2. Asadullah M, Bashir A, and Aleemi AR (2021). Forecasting exchange rates: An empirical application to Pakistani rupee. The Journal of Asian Finance, Economics and Business, 8(4): 339-347.   [Google Scholar]
  3. Ayakeme TI, Biu OE, Enegesele D, and Wonu N (2021). Forecasting of Bayelsa state internally generated revenue using ARIMA model and winter methods. International Journal of Statistics and Applied Mathematics, 6(1): 107-116.   [Google Scholar]
  4. Ceylan Z, Bulkan S, and Elevli S (2020). Prediction of medical waste generation using SVR, GM (1, 1) and ARIMA models: A case study for megacity Istanbul. Journal of Environmental Health Science and Engineering, 18(2): 687-697.   [Google Scholar] PMid:33312594 PMCid:PMC7721841
  5. Chen Y and Dai F (2020). Integrating SVR and ARIMA Approach to build the municipal solid waste generation prediction system. Journal of Computers, 31(3): 216-225.   [Google Scholar]
  6. Chintalapudi N, Battineni G, and Amenta F (2020). COVID-19 virus outbreak forecasting of registered and recovered cases after sixty day lockdown in Italy: A data driven model approach. Journal of Microbiology, Immunology and Infection, 53(3): 396-403.   [Google Scholar] PMid:32305271 PMCid:PMC7152918
  7. Emetere ME and Iroham CO (2021). Computational forecast of municipal waste in Lagos: What may happen in 2025? In the IOP Conference Series: Materials Science and Engineering. IOP Publishing, Bristol, UK: 012014.   [Google Scholar]
  8. Fattah J, Ezzine L, Aman Z, El Moussami H, and Lachhab A (2018). Forecasting of demand using ARIMA model. International Journal of Engineering Business Management, 10: 1-9.   [Google Scholar]
  9. Ferronato N and Torretta V (2019). Waste mismanagement in developing countries: A review of global issues. International Journal of Environmental Research and Public Health, 16(6): 1060.   [Google Scholar] PMid:30909625 PMCid:PMC6466021
  10. Hyndman RJ (2015). Measuring forecast accuracy. In: Gilliland M, Tashman L, and Sglavo U (Eds.), Business forecasting: Practical problems and solutions: 177-184. John Wiley & Sons, Hoboken, USA.   [Google Scholar]
  11. Kim H (2022). A finite sample correction for the panel Durbin–Watson test. Applied Economics, 54(28): 3197-3205.   [Google Scholar]
  12. Kumar S and Kumar R (2021). Forecasting of municipal solid waste generation using non-linear autoregressive (NAR) neural models. Waste Management, 121: 206-214.   [Google Scholar] PMid:33360819
  13. Mohamad NAJ, Yatim SRM, Abdullah S, Azmin MT, and Alwi N (2022). Forecasting municipal solid waste (MSW) generation in Klang, Selangor ssing artificial neural network (ANN). Malaysian Journal of Medicine and Health Sciences, 18(8): 151-158.   [Google Scholar]
  14. Mohamed IE (2008). Time series analysis using SAS-part I-the augmented Dickey-Fuller (ADF) test. In the SAS Conference Proceedings, Pittsburgh, USA.   [Google Scholar]
  15. Niu D, Wu F, Dai S, He S, and Wu B (2021). Detection of long-term effect in forecasting municipal solid waste using a long short-term memory neural network. Journal of Cleaner Production, 290: 125187.   [Google Scholar]
  16. Schwarz G (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2): 461-464.   [Google Scholar]
  17. Sharifah NSI and Latifah AM (2013). The challenge of future landfill: A case study of Malaysia. Journal of Toxicology and Environmental Health Sciences, 5(6): 86-96.   [Google Scholar]
  18. Siddiqua A, Hahladakis JN, and Al-Attiya WAK (2022). An overview of the environmental pollution and health effects associated with waste landfilling and open dumping. Environmental Science and Pollution Research, 29: 58514-58536.   [Google Scholar] PMid:35778661 PMCid:PMC9399006
  19. Sriploy S and Lertpocasombut K (2020). Industrial wastes to wastes disposal management by using box Jenkins-ARIMA models and created applications: Case study of four waste transport and disposal service providers in Thailand. EnvironmentAsia, 13(1): 124-139.   [Google Scholar]
  20. Wu F, Niu D, Dai S, and Wu B (2020). New insights into regional differences of the predictions of municipal solid waste generation rates using artificial neural networks. Waste Management, 107: 182-190.   [Google Scholar] PMid:32299033
  21. Zafra C, Ángel Y, and Torres E (2017). ARIMA analysis of the effect of land surface coverage on PM10 concentrations in a high-altitude megacity. Atmospheric Pollution Research, 8(4): 660-668.   [Google Scholar]
  22. Zulkipli F, Jamian NH, and Zulkifli IZ (2020). Forecasting model for organic waste generation at administration Cafe in UITM Tapah Campus. International Journal of Academic Research in Business and Social Sciences, 10(9): 1023-1032.   [Google Scholar]