Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting

•For the first time a LSTM-ED model is proposed to model multi-step-ahead flood forecasting.•Sequence-to-sequence learning converts an input sequence into an output sequence.•LSTM encoder-decoder models tackle the challenging sequence-to-sequence prediction.•The LSTM-ED model reduced RMSE by 3% (T +...

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Bibliographic Details
Published in:Journal of hydrology (Amsterdam) 2020-04, Vol.583, p.124631, Article 124631
Main Authors: Kao, I-Feng, Zhou, Yanlai, Chang, Li-Chiu, Chang, Fi-John
Format: Article
Language:English
Subjects:
case studies
data collection
Encoder-Decoder (ED) model
flood control
Flood forecast
hydrologic data
Long Short-Term Memory (LSTM)
meteorological data
model validation
neural networks
rain
Recurrent neural network (RNN)
runoff
Sequence-to-sequence
Taiwan
typhoons
watersheds
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