A Comparative Study of Machine Learning Models for Accurate E-Waste Prediction
The rapid growth of electrical and electronic equipment waste (e-waste) presents a major environmental challenge. Traditional linear production models fail to optimize resource recovery, while circular economy (CE) strategies remain underutilized due to inadequate forecasting methods. Given the high...
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Elsevier
2025-06-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025014586 |
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| author | Mohammed Algafri Mohammed Sayad Mohammad A.M. Abdel-Aal Ahmed M. Attia |
| author_facet | Mohammed Algafri Mohammed Sayad Mohammad A.M. Abdel-Aal Ahmed M. Attia |
| author_sort | Mohammed Algafri |
| collection | DOAJ |
| description | The rapid growth of electrical and electronic equipment waste (e-waste) presents a major environmental challenge. Traditional linear production models fail to optimize resource recovery, while circular economy (CE) strategies remain underutilized due to inadequate forecasting methods. Given the high-value materials in e-waste, accurate prediction is crucial for efficient recycling and regulatory planning. This study evaluates six Machine Learning (ML) models, Linear Regression, Regression Tree, Support Vector Regression, Ensemble Regression, Gaussian Process Regression (GPR), and Artificial Neural Networks, for e-waste forecasting. Using historical Malaysian e-waste data, models were trained and optimized in MATLAB, with performance assessed via RMSE, MAE, and R². Results show that GPR significantly outperforms all models, providing the highest accuracy and lowest prediction errors across all e-waste categories. Optimal GPR kernel functions vary by waste type, with Rational Quadratic, Squared Exponential, Matérn 5/2, and Exponential kernels producing the best results. SVR and ensemble trees also perform well but are slightly less reliable. These findings highlight the need for ML-driven forecasting in e-waste management policies. Integrating GPR into national recycling programs and Extended Producer Responsibility (EPR) policies can optimize collection systems and resource recovery. Future research should explore hybrid models and real-time forecasting to enhance predictive accuracy and sustainability. |
| format | Article |
| id | doaj-art-522be40ad98448bba65bdf33a28b8e15 |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
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| series | Results in Engineering |
| spelling | doaj-art-522be40ad98448bba65bdf33a28b8e152025-08-20T02:26:56ZengElsevierResults in Engineering2590-12302025-06-012610538810.1016/j.rineng.2025.105388A Comparative Study of Machine Learning Models for Accurate E-Waste PredictionMohammed Algafri0Mohammed Sayad1Mohammad A.M. Abdel-Aal2Ahmed M. Attia3Industrial and Systems Engineering Department, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi ArabiaIndustrial and Systems Engineering Department, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi ArabiaIndustrial and Systems Engineering Department, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia; Interdisciplinary Research Center for Smart Mobility and Logistics (IRC-SML), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia; Corresponding author.Industrial and Systems Engineering Department, King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia; Interdisciplinary Research Center for Smart Mobility and Logistics (IRC-SML), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi ArabiaThe rapid growth of electrical and electronic equipment waste (e-waste) presents a major environmental challenge. Traditional linear production models fail to optimize resource recovery, while circular economy (CE) strategies remain underutilized due to inadequate forecasting methods. Given the high-value materials in e-waste, accurate prediction is crucial for efficient recycling and regulatory planning. This study evaluates six Machine Learning (ML) models, Linear Regression, Regression Tree, Support Vector Regression, Ensemble Regression, Gaussian Process Regression (GPR), and Artificial Neural Networks, for e-waste forecasting. Using historical Malaysian e-waste data, models were trained and optimized in MATLAB, with performance assessed via RMSE, MAE, and R². Results show that GPR significantly outperforms all models, providing the highest accuracy and lowest prediction errors across all e-waste categories. Optimal GPR kernel functions vary by waste type, with Rational Quadratic, Squared Exponential, Matérn 5/2, and Exponential kernels producing the best results. SVR and ensemble trees also perform well but are slightly less reliable. These findings highlight the need for ML-driven forecasting in e-waste management policies. Integrating GPR into national recycling programs and Extended Producer Responsibility (EPR) policies can optimize collection systems and resource recovery. Future research should explore hybrid models and real-time forecasting to enhance predictive accuracy and sustainability.http://www.sciencedirect.com/science/article/pii/S2590123025014586Machine learningE-wasteCircular economyForecastingArtificial neural networks |
| spellingShingle | Mohammed Algafri Mohammed Sayad Mohammad A.M. Abdel-Aal Ahmed M. Attia A Comparative Study of Machine Learning Models for Accurate E-Waste Prediction Results in Engineering Machine learning E-waste Circular economy Forecasting Artificial neural networks |
| title | A Comparative Study of Machine Learning Models for Accurate E-Waste Prediction |
| title_full | A Comparative Study of Machine Learning Models for Accurate E-Waste Prediction |
| title_fullStr | A Comparative Study of Machine Learning Models for Accurate E-Waste Prediction |
| title_full_unstemmed | A Comparative Study of Machine Learning Models for Accurate E-Waste Prediction |
| title_short | A Comparative Study of Machine Learning Models for Accurate E-Waste Prediction |
| title_sort | comparative study of machine learning models for accurate e waste prediction |
| topic | Machine learning E-waste Circular economy Forecasting Artificial neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025014586 |
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