Using a physics-based hydrological model and storm transposition to investigate machine-learning algorithms for streamflow prediction

Author:

Gurbuz FarukORCID,Mudireddy AvinashORCID,Mantilla RicardoORCID,Xiao ShaopingORCID

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference54 articles.

1. The effect of calibration data length on the performance of a conceptual hydrological model versus LSTM and GRU: a case study for six basins from the CAMELS dataset;Ayzel;Comput. Geosci.,2021

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3. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, in;Cho,2014

4. The abuse of popular performance metrics in hydrologic modeling;Clark;Water Resour. Res.,2021

5. WaterBench-Iowa: a large-scale benchmark dataset for data-driven streamflow forecasting;Demir;Earth Syst. Sci. Data,2022

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