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

• Published in: International Journal of Advanced and Applied Sciences
• Main Author: Nasir N.; Shariff S.S.R.; Januri S.S.; Zulkipli F.; Md Yasin Z.A.M.
• Format: Article
• Language: English
• Published: Institute of Advanced Science Extension (IASE) 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85156209915&doi=10.21833%2fijaas.2023.04.009&partnerID=40&md5=f3c7295d3c84e49602fe92a4da0f356c

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/).