LSTM networks provide efficient cyanobacterial blooms forecasting even with incomplete spatio-temporal data

Fuente: arXiv

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Bibliographic Details
Main Authors:	Fournier, Claudia, Fernandez-Fernandez, Raul, Cirés, Samuel, López-Orozco, José A., Besada-Portas, Eva, Quesada, Antonio
Format:	Preprint
Published:	2024
Subjects:	Quantitative Methods Machine Learning Neural and Evolutionary Computing
Online Access:	Acceder al recurso
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Internet

https://arxiv.org/abs/2410.08237