PMID- 36612429
OWN - NLM
STAT- MEDLINE
DCOM- 20230110
LR  - 20230313
IS  - 1660-4601 (Electronic)
IS  - 1661-7827 (Print)
IS  - 1660-4601 (Linking)
VI  - 20
IP  - 1
DP  - 2022 Dec 21
TI  - Development of Machine Learning Model for Prediction of Demolition Waste 
      Generation Rate of Buildings in Redevelopment Areas.
LID - 10.3390/ijerph20010107 [doi]
LID - 107
AB  - 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.
FAU - Cha, Gi-Wook
AU  - Cha GW
AUID- ORCID: 0000-0001-8590-6482
AD  - School of Science and Technology Acceleration Engineering, Kyungpook National 
      University, Daegu 41566, Republic of Korea.
FAU - Choi, Se-Hyu
AU  - Choi SH
AD  - School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook 
      National University, Daegu 41566, Republic of Korea.
FAU - Hong, Won-Hwa
AU  - Hong WH
AD  - School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook 
      National University, Daegu 41566, Republic of Korea.
FAU - Park, Choon-Wook
AU  - Park CW
AD  - Industry Academic Cooperation Foundation, Kyungpook National University, Daegu 
      41566, Republic of Korea.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20221221
PL  - Switzerland
TA  - Int J Environ Res Public Health
JT  - International journal of environmental research and public health
JID - 101238455
SB  - IM
MH  - *Artificial Intelligence
MH  - Machine Learning
MH  - Algorithms
MH  - Neural Networks, Computer
MH  - *Waste Management/methods
MH  - Support Vector Machine
PMC - PMC9819715
OTO - NOTNLM
OT  - demolition waste
OT  - machine learning
OT  - optimal predictive model
OT  - redevelopment area
OT  - waste generation rate
OT  - waste management
COIS- The authors declare no conflict of interest.
EDAT- 2023/01/09 06:00
MHDA- 2023/01/11 06:00
PMCR- 2022/12/21
CRDT- 2023/01/08 01:10
PHST- 2022/11/24 00:00 [received]
PHST- 2022/12/14 00:00 [revised]
PHST- 2022/12/17 00:00 [accepted]
PHST- 2023/01/08 01:10 [entrez]
PHST- 2023/01/09 06:00 [pubmed]
PHST- 2023/01/11 06:00 [medline]
PHST- 2022/12/21 00:00 [pmc-release]
AID - ijerph20010107 [pii]
AID - ijerph-20-00107 [pii]
AID - 10.3390/ijerph20010107 [doi]
PST - epublish
SO  - Int J Environ Res Public Health. 2022 Dec 21;20(1):107. doi: 
      10.3390/ijerph20010107.