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Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas

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  • Gi-Wook Cha

    (School of Science and Technology Acceleration Engineering, Kyungpook National University, Daegu 41566, Republic of Korea)

  • Se-Hyu Choi

    (School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu 41566, Republic of Korea)

  • Won-Hwa Hong

    (School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu 41566, Republic of Korea)

  • Choon-Wook Park

    (Industry Academic Cooperation Foundation, Kyungpook National University, Daegu 41566, Republic of Korea)

Abstract

Owing to a rapid increase in waste, waste management has become essential, for which waste generation (WG) information has been effectively utilized. Various studies have recently focused on the development of reliable predictive models by applying artificial intelligence to the construction and prediction of WG information. In this study, research was conducted on the development of machine learning (ML) models for predicting the demolition waste generation rate (DWGR) of buildings in redevelopment areas in South Korea. Various ML algorithms (i.e., artificial neural network (ANN), K-nearest neighbors (KNN), linear regression (LR), random forest (RF), and support vector machine (SVM)) were applied to the development of an optimal predictive model, and the main hyper parameters (HPs) for each algorithm were optimized. The results suggest that ANN-ReLu (coefficient of determination (R 2 ) 0.900, the ratio of percent deviation (RPD) 3.16), SVM-polynomial (R 2 0.889, RPD 3.00), and ANN-logistic (R 2 0.883, RPD 2.92) are the best ML models for predicting the DWGR. They showed average errors of 7.3%, 7.4%, and 7.5%, respectively, compared to the average observed values, confirming the accurate predictive performance, and in the uncertainty analysis, the d-factor of the models appeared less than 1, showing that the presented models are reliable. Through a comparison with ML algorithms and HPs applied in previous related studies, the results herein also showed that the selection of various ML algorithms and HPs is important in developing optimal ML models for WG management.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:20:y:2022:i:1:p:107-:d:1010812
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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. Gi-Wook Cha & Hyeun Jun Moon & Young-Min Kim & Won-Hwa Hong & Jung-Ha Hwang & Won-Jun Park & Young-Chan Kim, 2020. "Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets," IJERPH, MDPI, vol. 17(19), pages 1-15, September.
    3. Maria Triassi & Rossella Alfano & Maddalena Illario & Antonio Nardone & Oreste Caporale & Paolo Montuori, 2015. "Environmental Pollution from Illegal Waste Disposal and Health Effects: A Review on the “Triangle of Death”," IJERPH, MDPI, vol. 12(2), pages 1-21, January.
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    1. Gi-Wook Cha & Choon-Wook Park & Young-Chan Kim & Hyeun Jun Moon, 2023. "Predicting Generation of Different Demolition Waste Types Using Simple Artificial Neural Networks," Sustainability, MDPI, vol. 15(23), pages 1-22, November.

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