Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method
Author:
Funder
Oak Ridge National Laboratory
Water Power Technologies Office
U.S. Department of Energy
UT-Battelle
Publisher
Elsevier BV
Subject
Earth and Planetary Sciences (miscellaneous),Water Science and Technology
Reference54 articles.
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3. A novel attention-based LSTM cell post-processor coupled with Bayesian optimization for streamflow prediction;Alizadeh;J. Hydrol.,2021
4. Comparative analysis of recurrent neural network architectures for reservoir inflow forecasting;Apaydin;Water,2020
5. Principles and practice of explainable machine learning;Belle;Front. Big Data,2021
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