Exploring the performance and interpretability of hybrid hydrologic model coupling physical mechanisms and deep learning

River flow forecasting through conceptual models part I — A discussion of principles

J.E. Nash, J. V. Sutcliffe · Journal of Hydrology · 1970 · 24K citations

Natural Resources, Hydrology, Water Resources Management +14

Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling

Hoshin V. Gupta, Harald Kling, Koray K. Yılmaz et al. · Journal of Hydrology · 2009 · 6.7K citations

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Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks

Frederik Kratzert, Daniel Klotz, Claire Brenner et al. · Hydrology and earth system sciences · 2018 · 1.8K citations · Full text

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Automatic early stopping using cross validation: quantifying the criteria

Lutz Prechelt · Neural Networks · 1998 · 984 citations

Computing, Data Science, Decision Sciences +5

Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance

Andrew J. Newman, Martyn Clark, K. M. Sampson et al. · Hydrology and earth system sciences · 2015 · 676 citations · Full text

Natural Resources, Earth Sciences, Hydrology +17