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

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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
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    References listed on IDEAS

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    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.
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