Machine learning models coupled with empirical mode decomposition for simulating monthly and yearly streamflows: a case study of three watersheds in Ontario, Canada

This paper presents a novel approach for enhancing long-term runoff simulations through the integration of empirical mode decomposition (EMD) with four machine learning (ML) models: ensemble, support vector machine (SVM), convolutional neural networks (CNN), and artificial neural networks with backp...

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Автори:	Peiman Parisouj, Changhyun Jun, Sayed M. Bateni, Essam Heggy, Shahab S. Band
Формат:	Artigo
Мова:	Inglês
Опубліковано:	Taylor & Francis Group 2023-12-01
Серія:	Engineering Applications of Computational Fluid Mechanics
Предмети:	Machine learning runoff estimation EMD method ensemble model
Онлайн доступ:	https://www.tandfonline.com/doi/10.1080/19942060.2023.2242445
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