Performance and uncertainty analysis in deep learning frameworks for streamflow forecasting via Monte Carlo dropout technique

Study Region: The research was conducted at the Red River Basin, Vietnam. Study Focus: This study evaluated the performance of six deep learning models (Standard LSTM (S_LSTM), Bidirectional LSTM (Bi_LSTM), LSTM with attention mechanism (At_LSTM), Advanced LSTM (Ad_LSTM), Temporal Convolutional Netw...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Xuan-Hien Le, Doan Van Binh, Giha Lee
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	Elsevier 2025-10-01
Sarja:	Journal of Hydrology: Regional Studies
Aiheet:	Feature Importance Monte Carlo Dropout Streamflow Prediction Temporal Convolutional Network (TCN) Uncertainty Quantification
Linkit:	http://www.sciencedirect.com/science/article/pii/S2214581825004975
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