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A Novel Ensemble Meta-Model for Predicting Demolition Solid Waste Generation

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Ensemble models improved prediction accuracy of demolition solid waste, suggesting enhanced management strategies.

The proposed stacked ensemble model achieves an R² score of 0.99995 in predicting demolition solid waste.
Low errors were observed across training, validation, and test datasets, outperforming classic baselines like SARIMA.
Ensemble machine learning methods such as random forest and xgboost were key in enhancing prediction reliability.
Improved forecasting capabilities can lead to better operational planning in demolition projects and waste management.

Tyagi et al. (2025) studied this question.

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View Full Paper

Ask AI

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Authors

Ensemble models improved prediction accuracy of demolition solid waste, suggesting enhanced management strategies.

The proposed stacked ensemble model achieves an R² score of 0.99995 in predicting demolition solid waste.
Low errors were observed across training, validation, and test datasets, outperforming classic baselines like SARIMA.
Ensemble machine learning methods such as random forest and xgboost were key in enhancing prediction reliability.
Improved forecasting capabilities can lead to better operational planning in demolition projects and waste management.

Tyagi et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark