IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v26y2024i5d10.1007_s10668-023-03882-x.html
   My bibliography  Save this article

Evaluating different machine learning models for predicting municipal solid waste generation: a case study of Malaysia

Author

Listed:
  • Sarmad Dashti Latif

    (Soran University, Soran
    Komar University of Science and Technology)

  • Nur Alyaa Binti Hazrin

    (Universiti Tenaga Nasional)

  • Mohammad K. Younes

    (Philadelphia University)

  • Ali Najah Ahmed

    (Universiti Tenaga Nasional
    Universiti Tenaga Nasional (UNITEN))

  • Ahmed Elshafie

    (University of Malaya (UM))

Abstract

It is crucial for developing countries such as Malaysia to be able to accurately predict future municipal solid waste generations in order to achieve high-quality waste management. The previous machine algorithm applied in the proposed study area Malaysia was an artificial neural network using NARX inputs to accommodate the need of forecasting municipal solid waste generations in Malaysia. However, this approach is not highly accurate in today’s higher progressive state. Therefore, one of the aims of this research was to investigate the use of machine learning algorithms and its benefits. The machine learning algorithms investigated are specifically Gaussian process regression (GPR), ensemble of trees and neural networks. Each of these algorithms has its many strengths that could be altered according to the needs of users. For instance, various versions of neural networks are widely used for predicting municipal solid waste which includes the current approach adapted in the proposed study area. The findings indicated that the bagged tree model currently developed is not suitable for plotting a linear prediction although it managed to obtain a high performance of coefficient of determination (R2) = 0.92. Regarding GPR and neural network, the accuracy of the models was very high when every variable is included as a scenario which gives a perfect R2 = 1.00. The findings also showed that GPR and neural networks had the least error with root mean square error (RMSE) of 0.00009748 and 0.00099684, and mean absolute error (MAE) of 0.000071824 and 0.000672810, respectively. This study managed to fill in the gap of using GPR for predicting municipal solid waste generation. The outcome of this study could be of direct interest to public and private solid waste management companies in order to effectively manage solid waste through predicting the municipal solid waste generation accurately.

Suggested Citation

  • Sarmad Dashti Latif & Nur Alyaa Binti Hazrin & Mohammad K. Younes & Ali Najah Ahmed & Ahmed Elshafie, 2024. "Evaluating different machine learning models for predicting municipal solid waste generation: a case study of Malaysia," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(5), pages 12489-12512, May.
    • Handle: RePEc:spr:endesu:v:26:y:2024:i:5:d:10.1007_s10668-023-03882-x
    DOI: 10.1007/s10668-023-03882-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-023-03882-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10668-023-03882-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Gi-Wook Cha & Hyeun-Jun Moon & Young-Chan Kim, 2021. "Comparison of Random Forest and Gradient Boosting Machine Models for Predicting Demolition Waste Based on Small Datasets and Categorical Variables," IJERPH, MDPI, vol. 18(16), pages 1-16, August.
    2. Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha & Wenying Wen, 2019. "Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods," Energies, MDPI, vol. 12(9), pages 1-17, May.
    3. Jinhui Liu & Qing Li & Wei Gu & Chen Wang, 2019. "The Impact of Consumption Patterns on the Generation of Municipal Solid Waste in China: Evidences from Provincial Data," IJERPH, MDPI, vol. 16(10), pages 1-19, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dimitrios Mouchtaris & Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2021. "Forecasting Natural Gas Spot Prices with Machine Learning," Energies, MDPI, vol. 14(18), pages 1-13, September.
    2. Stajić, Ljubiša & Praksová, Renáta & Brkić, Dejan & Praks, Pavel, 2024. "Estimation of global natural gas spot prices using big data and symbolic regression," Resources Policy, Elsevier, vol. 95(C).
    3. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
    4. Baihui Jin & Wei Li, 2023. "External Factors Impacting Residents’ Participation in Waste Sorting Using NCA and fsQCA Methods on Pilot Cities in China," IJERPH, MDPI, vol. 20(5), pages 1-21, February.
    5. Anna Kożuch & Dominika Cywicka & Marek Wieruszewski & Miloš Gejdoš & Krzysztof Adamowicz, 2025. "The Impact of Selected Market Factors on the Prices of Wood Industry By-Products in Poland in the Context of Climate Policy Changes," Energies, MDPI, vol. 18(16), pages 1-25, August.
    6. Laura Böhm & Sebastian Kolb & Thomas Plankenbühler & Jonas Miederer & Simon Markthaler & Jürgen Karl, 2023. "Short-Term Natural Gas and Carbon Price Forecasting Using Artificial Neural Networks," Energies, MDPI, vol. 16(18), pages 1-25, September.
    7. Yusi Liu & Zhijie Jiang & Wei Leng, 2025. "A Study on Predicting Natural Gas Prices Utilizing Ensemble Model," Sustainability, MDPI, vol. 17(18), pages 1-21, September.
    8. Wang, Jun & Cao, Junxing & Yuan, Shan & Cheng, Ming, 2021. "Short-term forecasting of natural gas prices by using a novel hybrid method based on a combination of the CEEMDAN-SE-and the PSO-ALS-optimized GRU network," Energy, Elsevier, vol. 233(C).
    9. Miguel A. Jaramillo-Morán & Agustín García-García, 2019. "Applying Artificial Neural Networks to Forecast European Union Allowance Prices: The Effect of Information from Pollutant-Related Sectors," Energies, MDPI, vol. 12(23), pages 1-18, November.
    10. Gi-Wook Cha & Won-Hwa Hong & Se-Hyu Choi & Young-Chan Kim, 2023. "Developing an Optimal Ensemble Model to Estimate Building Demolition Waste Generation Rate," Sustainability, MDPI, vol. 15(13), pages 1-20, June.
    11. Gi-Wook Cha & Se-Hyu Choi & Won-Hwa Hong & Choon-Wook Park, 2023. "Developing a Prediction Model of Demolition-Waste Generation-Rate via Principal Component Analysis," IJERPH, MDPI, vol. 20(4), pages 1-15, February.
    12. Wu, Xuepin & Ma, Yongjun, 2023. "Research on the comparison effect of urban residents' consumption," Journal of Business Research, Elsevier, vol. 160(C).
    13. Edza Aria Wikurendra & Arnold Csonka & Imre Nagy & Globila Nurika, 2024. "Urbanization and Benefit of Integration Circular Economy into Waste Management in Indonesia: A Review," Circular Economy and Sustainability, Springer, vol. 4(2), pages 1219-1248, June.
    14. Weibo Yin & Qingfeng Hu & Jinping Liu & Peipei He & Dantong Zhu & Abdolhossein Boali, 2024. "Assessing Climate and Land-Use Change Scenarios on Future Desertification in Northeast Iran: A Data Mining and Google Earth Engine-Based Approach," Land, MDPI, vol. 13(11), pages 1-16, October.
    15. Yadong Pei & Chiou-Jye Huang & Yamin Shen & Mingyue Wang, 2023. "A Novel Model for Spot Price Forecast of Natural Gas Based on Temporal Convolutional Network," Energies, MDPI, vol. 16(5), pages 1-15, February.
    16. Haowen Bao & Yongmiao Hong & Yuying Sun & Shouyang Wang, 2025. "A Novel Hybrid Nonlinear Forecasting Model for Interval‐Valued Gas Prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(5), pages 1826-1848, August.
    17. Jonathan Berrisch & Florian Ziel, 2022. "Distributional modeling and forecasting of natural gas prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1065-1086, September.
    18. Aidoni Artemis & Kofidis Konstantinos & Urse Cristian & Stoica Alexandru, 2025. "Predictive Modeling for Natural Gas Prices in Romania Using Time Series Data and Average Temperature," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 19(1), pages 2505-2515.
    19. Gi-Wook Cha & Se-Hyu Choi & Won-Hwa Hong & Choon-Wook Park, 2022. "Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas," IJERPH, MDPI, vol. 20(1), pages 1-17, December.
    20. Yong-Hyong Kim & Song-Jun Ham & Chong-Sim Ri & Won-Hyok Kim & Wi-Song Ri, 2025. "Application of empirical wavelet transform, particle swarm optimization, gravitational search algorithm and long short-term memory neural network to copper price forecasting," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 24(1), pages 151-169, January.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:endesu:v:26:y:2024:i:5:d:10.1007_s10668-023-03882-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.