A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting

•Quantile-based encoder-decoder models proposed for probabilistic runoff forecasting.•Proposed models more accurate and reliable than benchmarks for 3 test catchments.•Wavelet selection can be used to improve forecast accuracy and reliability.•Model performance sensitive to precipitation forecast ac...

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Vydané v:	Journal of hydrology (Amsterdam) Ročník 619; s. 129269
Hlavní autori:	Jahangir, Mohammad Sina, You, John, Quilty, John
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.04.2023
Predmet:	data collection Deep learning Encoder-decoder Hydrological forecasting LSTM meteorology neural networks rain runoff Runoff forecasting snowmelt watersheds wavelet Wavelet decomposition Deep learning LSTM Encoder-decoder Runoff forecasting Hydrological forecasting Wavelet decomposition
ISSN:	0022-1694, 1879-2707
On-line prístup:	Získať plný text
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