Data-driven prediction of construction and demolition waste generation using limited datasets in developing countries: an optimized extreme gradient boosting approach

Ahmed Maged; Nehal Elshaboury; Lukman Akanbi

doi:10.1007/s10668-024-04814-z

Data-driven prediction of construction and demolition waste generation using limited datasets in developing countries: an optimized extreme gradient boosting approach

Ahmed Maged, Nehal Elshaboury^*, Lukman Akanbi

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	1
Number of pages	25
Journal	Environment, Development and Sustainability
DOIs	https://doi.org/10.1007/s10668-024-04814-z https://doi.org/10.1007/s10668-024-04814-z
Publication status	Published (VoR) - 17 Apr 2024

Keywords

Bayesian optimization
Construction and demolition waste
Genetic algorithm
Limited data
Optimized machine learning model
Particle swarm optimization
Waste quantification

Access to Document

10.1007/s10668-024-04814-z
10.1007/s10668-024-04814-zLicence: Unspecified

Cite this

@article{bcd706c0f893461aba7023b846d037a6,

title = "Data-driven prediction of construction and demolition waste generation using limited datasets in developing countries: an optimized extreme gradient boosting approach",

keywords = "Bayesian optimization, Construction and demolition waste, Genetic algorithm, Limited data, Optimized machine learning model, Particle swarm optimization, Waste quantification",

author = "Ahmed Maged and Nehal Elshaboury and Lukman Akanbi",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive licence to Springer Nature B.V. 2024.",

year = "2024",

month = apr,

day = "17",

doi = "10.1007/s10668-024-04814-z",

language = "English",

pages = "1",

journal = "Environment, Development and Sustainability",

issn = "1387-585X",

publisher = "Springer",

}

TY - JOUR

T1 - Data-driven prediction of construction and demolition waste generation using limited datasets in developing countries

T2 - an optimized extreme gradient boosting approach

AU - Maged, Ahmed

AU - Elshaboury, Nehal

AU - Akanbi, Lukman

N1 - Publisher Copyright: © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

PY - 2024/4/17

Y1 - 2024/4/17

KW - Bayesian optimization

KW - Construction and demolition waste

KW - Genetic algorithm

KW - Limited data

KW - Optimized machine learning model

KW - Particle swarm optimization

KW - Waste quantification

UR - http://www.scopus.com/inward/record.url?scp=85190651488&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85190651488&partnerID=8YFLogxK

U2 - 10.1007/s10668-024-04814-z

DO - 10.1007/s10668-024-04814-z

M3 - Article

AN - SCOPUS:85190651488

SN - 1387-585X

SP - 1

JO - Environment, Development and Sustainability

JF - Environment, Development and Sustainability

ER -

Data-driven prediction of construction and demolition waste generation using limited datasets in developing countries: an optimized extreme gradient boosting approach

Keywords

Access to Document

Other files and links

Fingerprint

Cite this