Comparison of Random Forest and Gradient Boosting Machine Models for Predicting Demolition Waste Based on Small Datasets and Categorical Variables
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Comparison of Random Forest and Gradient Boosting Machine Models for Predicting Demolition Waste Based on Small Datasets and Categorical Variables
Authors
Keywords
-
Journal
International Journal of Environmental Research and Public Health
Volume 18, Issue 16, Pages 8530
Publisher
MDPI AG
Online
2021-08-12
DOI
10.3390/ijerph18168530
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Development of machine learning - based models to forecast solid waste generation in residential areas: A case study from Vietnam
- (2021) X. Cuong Nguyen et al. RESOURCES CONSERVATION AND RECYCLING
- Evaluating recycling potential of demolition waste considering building structure types: A study in South Korea
- (2020) Gi-Wook Cha et al. JOURNAL OF CLEANER PRODUCTION
- Artificial intelligence applications in solid waste management: A systematic research review
- (2020) Mohamed Abdallah et al. WASTE MANAGEMENT
- Application of artificial intelligence neural network modeling to predict the generation of domestic, commercial and construction wastes
- (2020) Gulnur Coskuner et al. WASTE MANAGEMENT & RESEARCH
- Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets
- (2020) Gi-Wook Cha et al. International Journal of Environmental Research and Public Health
- Comparative study of predicting hospital solid waste generation using multiple linear regression and artificial intelligence
- (2019) Somayeh Golbaz et al. Journal of Environmental Health Science and Engineering
- Slope stability prediction using integrated metaheuristic and machine learning approaches: A comparative study
- (2018) Chongchong Qi et al. COMPUTERS & INDUSTRIAL ENGINEERING
- Municipal solid waste generation in China: influencing factor analysis and multi-model forecasting
- (2018) Leaksmy Chhay et al. Journal of Material Cycles and Waste Management
- Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches
- (2018) Miyuru Kannangara et al. WASTE MANAGEMENT
- Estimation of the generation rate of different types of plastic wastes and possible revenue recovery from informal recycling
- (2018) Atul Kumar et al. WASTE MANAGEMENT
- New approach for forecasting demolition waste generation using chi-squared automatic interaction detection (CHAID) method
- (2017) Gi-Wook Cha et al. JOURNAL OF CLEANER PRODUCTION
- Development of a hybrid model to predict construction and demolition waste: China as a case study
- (2017) Yiliao Song et al. WASTE MANAGEMENT
- Patterns of waste generation: A gradient boosting model for short-term waste prediction in New York City
- (2017) Nicholas E. Johnson et al. WASTE MANAGEMENT
- Efficient Leave-One-Out Cross-Validation-based Regularized Extreme Learning Machine
- (2016) Zhifei Shao et al. NEUROCOMPUTING
- Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran
- (2016) Sama Azadi et al. WASTE MANAGEMENT
- Identifying best design strategies for construction waste minimization
- (2015) Jiayuan Wang et al. JOURNAL OF CLEANER PRODUCTION
- Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation
- (2015) Tzu-Tsung Wong PATTERN RECOGNITION
- Composition and leaching of construction and demolition waste: Inorganic elements and organic compounds
- (2014) Stefania Butera et al. JOURNAL OF HAZARDOUS MATERIALS
- A model for estimating construction waste generation index for building project in China
- (2013) Jingru Li et al. RESOURCES CONSERVATION AND RECYCLING
- An Evaluation of Bagging, Boosting, and Random Forests for Land-Cover Classification in Cape Cod, Massachusetts, USA
- (2012) Bardan Ghimire et al. GIScience & Remote Sensing
- Longterm forecasting of solid waste generation by the artificial neural networks
- (2011) Mohammad Ali Abdoli et al. Environmental Progress & Sustainable Energy
- A web-based Decision Support System for the optimal management of construction and demolition waste
- (2011) G. Banias et al. WASTE MANAGEMENT
- An empirical investigation of construction and demolition waste generation rates in Shenzhen city, South China
- (2011) Weisheng Lu et al. WASTE MANAGEMENT
- A model for quantifying construction waste in projects according to the European waste list
- (2011) C. Llatas WASTE MANAGEMENT
- A framework for understanding waste management studies in construction
- (2011) Weisheng Lu et al. WASTE MANAGEMENT
- Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction
- (2009) Roohollah Noori et al. JOURNAL OF ENVIRONMENTAL MANAGEMENT
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started