On the role of the architecture for spring discharge prediction with deep learning approaches
Author:
Affiliation:
1. Department of Environmental and Geosciences Sam Houston State University Huntsville Texas USA
2. Department of Electrical and Computer Engineering Texas A&M University College Station Texas USA
Publisher
Wiley
Subject
Water Science and Technology
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.14737
Reference39 articles.
1. Simulation of karst spring discharge using a combination of time–frequency analysis methods and long short-term memory neural networks
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4. Cho K. vanMerrienboer B. Gulcehre C. Bahdanau D. Bougares F. Schwenk H. &Bengio Y.(2014).Learning phrase representations using RNN encoder‐decoder for statistical machine translation.arxiv:1406.1078.https://doi.org/10.48550/ARXIV.1406.1078
5. Effective improvement of multi-step-ahead flood forecasting accuracy through encoder-decoder with an exogenous input structure
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