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The study presents a stacking ensemble learning framework for predicting annual river ice breakup dates (BDs) using advanced machine learning techniques. It combines Bayesian regularization back-propagation artificial neural networks (BRANN) and adaptive neuro fuzzy inference systems (ANFIS) to improve prediction accuracy, particularly for the Athabasca River in Alberta, Canada. Results indicate that the proposed framework outperforms traditional models, providing a promising approach for managing ice-relat

Author

Hanh Nguyen Duc

Language

EN