A Method to improve the performance of support vector machine regression model for predicting demolition waste generation using categorical principal components analysis

Giwook Cha; Hyeunjun Moon; Jinho Kim

doi:10.22712/susb.20210023

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2021 Vol.12, Issue 3 Preview Page

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A Method to improve the performance of support vector machine regression model for predicting demolition waste generation using categorical principal components analysis

30 September 2021. pp. 282-294

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Abstract

To develop machine learning (ML)-based waste generation predictive models, many researchers are applying various artificial intelligence algorithms while researching predictive performance improvement according to the data characteristics. In this study, we worked on a hybrid demolition waster generation (DWG) predictive model to improve the performance of a small dataset comprising categorical variables. For this, we constructed a DWG dataset from 690 buildings. We adopted a support vector machine regression (SVMR) model to develop a DWG predictive model and applied the categorical principal components analysis (CATPCA) technique for input variable processing along with data pre-processing to improve predictive performance. Furthermore, to improve the predictive performance of the predicate model, we optimized the hyper-parameters for the SVMR algorithm before developing the model and developed SVMR and CATPCA-SVMR DWG predictive models, respectively. Leave one out cross-validation (LOOCV) was used for the validation of the predictive models. In the research results, the CATPCA-SVMR DWG predictive model (R² = 0.594, R = 0.770) developed based on a dataset with categorical variables converted into input variables through the application of the CATPCA technology showed significantly better predictive performance than the SVMR DWG predictive model (R² = 0007, R = 0.083). Based on this, in this study, we proposed a novel hybrid ML model development scheme, which has not been researched in the construction and demolition (C&D) waste management field until now. The results of this study are expected to help mitigate the data requirements in constructing DWG information required for DW management strategies and develop an appropriate DWG predictive model in various data environments.

Keywords

machine learning

demolition waste

predictive model

support vector machine

categorical principal components analysis

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Information

Publisher :Sustainable Building Research Center (ERC) Innovative Durable Building and Infrastructure Research Center
Publisher(Ko) :건설구조물 내구성혁신 연구센터
Journal Title :International Journal of Sustainable Building Technology and Urban Development
Volume : 12
No :3
Pages :282-294
Received Date : 2021-09-14
Accepted Date : 2021-09-30
DOI :https://doi.org/10.22712/susb.20210023

International Journal of Sustainable Building Technology and Urban Development ISSN:2093-761X(Print) 2093-7628(Online)

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