Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets

Recently, artificial intelligence (AI) technologies have been employed to predict construction and demolition (C&D) waste generation. However, most studies have used machine learning models with continuous data input variables, applying algorithms, such as artificial neural networks, adaptive ne...

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Veröffentlicht in:	International journal of environmental research and public health 2020-09, Vol.17 (19), p.6997
Hauptverfasser:	Cha, Gi-Wook, Moon, Hyeun Jun, Kim, Young-Min, Hong, Won-Hwa, Hwang, Jung-Ha, Park, Won-Jun, Kim, Young-Chan
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptive systems Algorithms Artificial Intelligence Concrete Construction Construction Industry Construction materials Correlation coefficient Correlation coefficients Datasets Demolition Feature selection Fuzzy logic Fuzzy systems Genetic algorithms Genetic analysis Gloss Learning algorithms Machine Learning Methods Neural networks Neural Networks, Computer Prediction models Regression analysis Roofing Solid Waste Statistical analysis Support Vector Machine Support vector machines Variables Waste disposal Waste management
Online-Zugang:	Volltext
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