Comparison of Random Forest and Gradient Boosting Machine Models for Predicting Demolition Waste Based on Small Datasets and Categorical Variables

• Published in: International journal of environmental research and public health 2021-08, Vol.18 (16), p.8530
• Main Authors: Cha, Gi-Wook, Moon, Hyeun-Jun, Kim, Young-Chan
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Bias; Big Data; Construction; Construction industry; Datasets; Demolition; Information management; Learning algorithms; Machine Learning; Performance evaluation; Prediction models; Standardization; Support vector machines; Waste disposal; Waste Management
• ISSN: 1660-4601; 1661-7827; 1660-4601
• DOI: 10.3390/ijerph18168530

Construction and demolition waste (DW) generation information has been recognized as a tool for providing useful information for waste management. Recently, numerous researchers have actively utilized artificial intelligence technology to establish accurate waste generation information. This study investigated the development of machine learning predictive models that can achieve predictive performance on small datasets composed of categorical variables. To this end, the random forest (RF) and gradient boosting machine (GBM) algorithms were adopted. To develop the models, 690 building datasets were established using data preprocessing and standardization. Hyperparameter tuning was performed to develop the RF and GBM models. The model performances were evaluated using the leave-one-out cross-validation technique. The study demonstrated that, for small datasets comprising mainly categorical variables, the bagging technique (RF) predictions were more stable and accurate than those of the boosting technique (GBM). However, GBM models demonstrated excellent predictive performance in some DW predictive models. Furthermore, the RF and GBM predictive models demonstrated significantly differing performance across different types of DW. Certain RF and GBM models demonstrated relatively low predictive performance. However, the remaining predictive models all demonstrated excellent predictive performance at values > 0.6, and values > 0.8. Such differences are mainly because of the characteristics of features applied to model development; we expect the application of additional features to improve the performance of the predictive models. The 11 DW predictive models developed in this study will be useful for establishing detailed DW management strategies.