Semisupervised machine learning classification framework for material intensity parameters of residential buildings.
The material intensity (MI) parameter plays an important role when determining amounts of material stocks, material inflows, and material outflows in material stock models. Recently, several studies have summarized MI parameter information for buildings from around the globe into a single database....
| Publicado en: | Journal of Industrial Ecology Vol. 26; no. 1; pp. 72 - 88 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Springer Nature
Feb2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=155254434&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 155254434 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10881980 FL1 jtl: Journal of Industrial Ecology issn: 10881980 maglogo: Y pubinfo: dt: Feb2022 vid: 26 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 155254434 10.1111/jiec.13174 ppf: 72 ppct: 16 formats: tig: atl: Semisupervised machine learning classification framework for material intensity parameters of residential buildings. aug: au: Vilaysouk, Xaysackda Saypadith, Savath Hashimoto, Seiji affil: Research Organization of Science and Technology, Ritsumeikan University, Shiga, Japan Department of Computer Engineering and Information Technology, Faculty of Engineering, National University of Laos, Vientiane, Laos Graduate School of Information Science and Technology, Osaka University, Osaka, Japan College of Science and Engineering, Ritsumeikan University, Shiga, Japan su: Machine learning Probability density function Lognormal distribution Weibull distribution Gamma distributions sug: subj: Machine learning Probability density function Lognormal distribution Weibull distribution Gamma distributions keyword: conditional inference tree industrial ecology k‐means material stock model residential buildings semisupervised machine learning conditional inference tree industrial ecology k‐means material stock model residential buildings semisupervised machine learning ab: The material intensity (MI) parameter plays an important role when determining amounts of material stocks, material inflows, and material outflows in material stock models. Recently, several studies have summarized MI parameter information for buildings from around the globe into a single database. Nevertheless, insufficiencies of building type information have led to difficulties when using MI data. This study used semisupervised machine learning to classify MI. An open database of MI parameters of buildings was used as input data for our semisupervised machine learning model. We used material composition data of MI as feature data fed into our machine learning (ML) model. Attribute information of those data points belongs to clusters obtained from the original database was used as information to discover building characteristics of buildings in each building of those clusters to assign building labels for data points of the original dataset. Experiment results revealed seven building clusters in the studied dataset. The probability density function of MI of three building clusters follows a Weibull distribution. The remaining clusters follow gamma and lognormal distributions. Building type labels inferred from the results are useful as additional attributes to the original dataset, providing a new dataset of MI that can be adapted easily for other studies when country‐specific MI data are not available. A decision tree for finding appropriate MI parameters was also introduced. The classification model accuracy was 92.73%, which was achieved using only 201 data points. The proposed framework presents possibilities for application to other MI studies. This article met the requirements for a Gold‐Gold JIE data openness badge described at http://jie.click/badges. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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