2023
DOI: 10.1016/j.ecolind.2023.111265
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Separating climate change and human activities' effects on flow regime with hydrological model error correction

Qin Wang,
Yong Liu,
Yintang Wang
et al.
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Cited by 2 publications
(1 citation statement)
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“…However, human activities interfere with the natural water cycle (e.g., land cover changes, hydraulic construction and water withdrawal), making hydrological modeling highly uncertain [23][24][25] . To simulate water cycle processes under climate change without disturbance, the Long Short-Term Memory (LSTM) model is considered as a helpful approach to assimilate the effects of climate change and human activities 26,27 . LSTM models can simultaneously memorize short-term and long-term streamflow series and address the forgetting of long series, improving the accuracy of hydrological modeling [28][29][30] .…”
Section: Introductionmentioning
confidence: 99%
“…However, human activities interfere with the natural water cycle (e.g., land cover changes, hydraulic construction and water withdrawal), making hydrological modeling highly uncertain [23][24][25] . To simulate water cycle processes under climate change without disturbance, the Long Short-Term Memory (LSTM) model is considered as a helpful approach to assimilate the effects of climate change and human activities 26,27 . LSTM models can simultaneously memorize short-term and long-term streamflow series and address the forgetting of long series, improving the accuracy of hydrological modeling [28][29][30] .…”
Section: Introductionmentioning
confidence: 99%