A Comparative Analysis of Gaussian Process Regression and Support Vector Machines in Predicting Carbon Emissions from Building Construction Activities

Authors

  • Rufaizal Che Mamat Centre of Green Technology for Sustainable Cities, Department of Civil Engineering, Politeknik Ungku Omar, Jalan Raja Musa Mahadi, 31400 Ipoh, Perak, Malaysia
  • Azuin Ramli Centre of Excellence for Research and Innovation, Politeknik Ungku Omar, Jalan Raja Musa Mahadi, 31400 Ipoh, Perak, Malaysia
  • Aminah Bibi Bawamohiddin Department of Information Technology and Telecommunications, Politeknik Ungku Omar, Jalan Raja Musa Mahadi, 31400 Ipoh Perak, Malaysia

DOI:

https://doi.org/10.37934/aram.131.1.185196

Keywords:

Gaussian process regression, support vector machine, carbon emissions, sustainable construction, predictive modeling

Abstract

The increasing effects of climate change require the accurate measurement and prediction of carbon emissions from building construction operations. This study aims to assess the performance of Gaussian Process Regression (GPR) and Support Vector Machine (SVM) in predicting emissions and determine which model is best suited for improving sustainable construction practices. The GPR and SVM models were trained and tested using empirical datasets with varying parameter values. Model performance is evaluated using correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE). The analysis reveals that GPR consistently performs better than SVM across diverse datasets, especially in intricate and varied data situations. The superior performance of GPR highlights its robustness and versatility in handling the complexities of construction-related emissions data. The results indicate that GPR has the potential to assist significantly many stakeholders in the construction sector, including planners and environmental managers, in making better-informed decisions that are in line with carbon reduction objectives. Further studies could examine the incorporation of further predictive variables into the GPR model, such as economic indicators and the effects of material innovation.

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Author Biographies

Rufaizal Che Mamat, Centre of Green Technology for Sustainable Cities, Department of Civil Engineering, Politeknik Ungku Omar, Jalan Raja Musa Mahadi, 31400 Ipoh, Perak, Malaysia

rufaizal.cm@gmail.com

Azuin Ramli, Centre of Excellence for Research and Innovation, Politeknik Ungku Omar, Jalan Raja Musa Mahadi, 31400 Ipoh, Perak, Malaysia

azuin.ramli@puo.edu.my

Aminah Bibi Bawamohiddin, Department of Information Technology and Telecommunications, Politeknik Ungku Omar, Jalan Raja Musa Mahadi, 31400 Ipoh Perak, Malaysia

aminahbibi@puo.edu.my

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Published

2024-12-30

How to Cite

Che Mamat, R., Ramli, A. ., & Bawamohiddin, A. B. . (2024). A Comparative Analysis of Gaussian Process Regression and Support Vector Machines in Predicting Carbon Emissions from Building Construction Activities. Journal of Advanced Research in Applied Mechanics, 131(1), 185–196. https://doi.org/10.37934/aram.131.1.185196

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