A comparative analysis of artificial intelligence techniques for carbon emission predictions in the construction industry
The construction industry significantly contributes to global carbon emissions, necessitating urgent mitigation measures. This study addresses the challenge of predicting carbon emissions during construction projects using advanced artificial intelligence (AI) techniques. The performance of two AI models, Random Forests (RF) and Support Vector Machines (SVM), is compared to determine their effectiveness in forecasting emissions based on construction materials, techniques and project scale. Predictive models were developed using a dataset derived from previous research and real-world cons