Assessing the simulation of streamflow with the LSTM model across the continental United States using the MOPEX dataset
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-08922-1.pdf
Reference55 articles.
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3. Shen Y, Wang S, Zhang B, Zhu J (2022) Development of a stochastic hydrological modeling system for improving ensemble streamflow prediction. J Hydrol 608:127683
4. Kişi Ö (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng 12(5):532–539
5. Liu J et al (2022) Ensemble streamflow forecasting over a cascade reservoir catchment with integrated hydrometeorological modeling and machine learning. Hydrol Earth Syst Sci 26(2):265–278
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1. Comparing a long short-term memory (LSTM) neural network with a physically-based hydrological model for streamflow forecasting over a Canadian catchment;Journal of Hydrology;2023-12
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