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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| Vydáno v: | Journal of hydrology (Amsterdam) Ročník 583; s. 124631 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.04.2020
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| Témata: | |
| ISSN: | 0022-1694, 1879-2707 |
| On-line přístup: | Získat plný text |
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| Abstract | •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 + 1) to 38% (T + 6) as compared to the benchmark.•The LSTM-ED model can provide reliable and accurate multi-step-ahead flood forecasts.
Operational flood control systems depend on reliable and accurate forecasts with a suitable lead time to take necessary actions against flooding. This study proposed a Long Short-Term Memory based Encoder-Decoder (LSTM-ED) model for multi-step-ahead flood forecasting for the first time. The Shihmen Reservoir catchment in Taiwan constituted the case study. A total of 12,216 hourly hydrological data collected from 23 typhoon events were allocated into three datasets for model training, validation, and testing. The input sequence of the model contained hourly reservoir inflows and rainfall data (traced back to the previous 8 h) of ten gauge stations, and the output sequence stepped into 1- up to 6-hour-ahead reservoir inflow forecasts. A feed forward neural network-based Encoder-Decoder (FFNN-ED) model was established for comparison purposes. This study conducted model training a number of times with various initial weights to evaluate the accuracy, stability, and reliability of the constructed FFNN-ED and LSTM-ED models. The results demonstrated that both models, in general, could provide suitable multi-step ahead forecasts, and the proposed LSTM-ED model not only could effectively mimic the long-term dependence between rainfall and runoff sequences but also could make more reliable and accurate flood forecasts than the FFNN-ED model. Concerning the time delay between the time horizons of model inputs (rainfall) and model outputs (runoff), the impact assessment of this time-delay on model performance indicated that the LSTM-ED model achieved similar forecast performance when fed with antecedent rainfall either at a shorter horizon of 4 h in the past (T − 4) or at horizons longer than 7 h in the past (>T − 7). We conclude that the proposed LSTM-ED that translates and links the rainfall sequence with the runoff sequence can improve the reliability of flood forecasting and increase the interpretability of model internals. |
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| AbstractList | Operational flood control systems depend on reliable and accurate forecasts with a suitable lead time to take necessary actions against flooding. This study proposed a Long Short-Term Memory based Encoder-Decoder (LSTM-ED) model for multi-step-ahead flood forecasting for the first time. The Shihmen Reservoir catchment in Taiwan constituted the case study. A total of 12,216 hourly hydrological data collected from 23 typhoon events were allocated into three datasets for model training, validation, and testing. The input sequence of the model contained hourly reservoir inflows and rainfall data (traced back to the previous 8 h) of ten gauge stations, and the output sequence stepped into 1- up to 6-hour-ahead reservoir inflow forecasts. A feed forward neural network-based Encoder-Decoder (FFNN-ED) model was established for comparison purposes. This study conducted model training a number of times with various initial weights to evaluate the accuracy, stability, and reliability of the constructed FFNN-ED and LSTM-ED models. The results demonstrated that both models, in general, could provide suitable multi-step ahead forecasts, and the proposed LSTM-ED model not only could effectively mimic the long-term dependence between rainfall and runoff sequences but also could make more reliable and accurate flood forecasts than the FFNN-ED model. Concerning the time delay between the time horizons of model inputs (rainfall) and model outputs (runoff), the impact assessment of this time-delay on model performance indicated that the LSTM-ED model achieved similar forecast performance when fed with antecedent rainfall either at a shorter horizon of 4 h in the past (T − 4) or at horizons longer than 7 h in the past (>T − 7). We conclude that the proposed LSTM-ED that translates and links the rainfall sequence with the runoff sequence can improve the reliability of flood forecasting and increase the interpretability of model internals. •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 + 1) to 38% (T + 6) as compared to the benchmark.•The LSTM-ED model can provide reliable and accurate multi-step-ahead flood forecasts. Operational flood control systems depend on reliable and accurate forecasts with a suitable lead time to take necessary actions against flooding. This study proposed a Long Short-Term Memory based Encoder-Decoder (LSTM-ED) model for multi-step-ahead flood forecasting for the first time. The Shihmen Reservoir catchment in Taiwan constituted the case study. A total of 12,216 hourly hydrological data collected from 23 typhoon events were allocated into three datasets for model training, validation, and testing. The input sequence of the model contained hourly reservoir inflows and rainfall data (traced back to the previous 8 h) of ten gauge stations, and the output sequence stepped into 1- up to 6-hour-ahead reservoir inflow forecasts. A feed forward neural network-based Encoder-Decoder (FFNN-ED) model was established for comparison purposes. This study conducted model training a number of times with various initial weights to evaluate the accuracy, stability, and reliability of the constructed FFNN-ED and LSTM-ED models. The results demonstrated that both models, in general, could provide suitable multi-step ahead forecasts, and the proposed LSTM-ED model not only could effectively mimic the long-term dependence between rainfall and runoff sequences but also could make more reliable and accurate flood forecasts than the FFNN-ED model. Concerning the time delay between the time horizons of model inputs (rainfall) and model outputs (runoff), the impact assessment of this time-delay on model performance indicated that the LSTM-ED model achieved similar forecast performance when fed with antecedent rainfall either at a shorter horizon of 4 h in the past (T − 4) or at horizons longer than 7 h in the past (>T − 7). We conclude that the proposed LSTM-ED that translates and links the rainfall sequence with the runoff sequence can improve the reliability of flood forecasting and increase the interpretability of model internals. |
| ArticleNumber | 124631 |
| Author | Chang, Li-Chiu Kao, I-Feng Chang, Fi-John Zhou, Yanlai |
| Author_xml | – sequence: 1 givenname: I-Feng surname: Kao fullname: Kao, I-Feng organization: Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan – sequence: 2 givenname: Yanlai orcidid: 0000-0002-5447-2420 surname: Zhou fullname: Zhou, Yanlai organization: Department of Geosciences, University of Oslo, P.O. Box 1047 Blindern, N-0316 Oslo, Norway – sequence: 3 givenname: Li-Chiu surname: Chang fullname: Chang, Li-Chiu organization: Department of Water Resources and Environmental Engineering, Tamkang University, New Taipei City 25137, Taiwan – sequence: 4 givenname: Fi-John orcidid: 0000-0002-1655-8573 surname: Chang fullname: Chang, Fi-John email: changfj@ntu.edu.tw organization: Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan |
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| Copyright | 2020 Elsevier B.V. |
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| Snippet | •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... Operational flood control systems depend on reliable and accurate forecasts with a suitable lead time to take necessary actions against flooding. This study... |
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| SubjectTerms | 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 |
| Title | Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting |
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