Developing an Optimal Ensemble Model to Estimate Building Demolition Waste Generation Rate
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- 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.
- 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.
- Andersen, Frits Møller & Larsen, Helge & Skovgaard, Mette & Moll, Stephan & Isoard, Stéphane, 2007. "A European model for waste and material flows," Resources, Conservation & Recycling, Elsevier, vol. 49(4), pages 421-435.
- Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
- Håvard Bergsdal & Rolf André Bohne & Helge Brattebø, 2007. "Projection of Construction and Demolition Waste in Norway," Journal of Industrial Ecology, Yale University, vol. 11(3), pages 27-39, July.
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Keywords
machine learning; random forest; extreme gradient boost; construction and demolition; waste generation;All these keywords.
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