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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| Cites_doi | 10.1016/j.jhydrol.2015.11.050 10.3390/w11071387 10.1016/j.jhydrol.2014.03.057 10.1016/j.jhydrol.2016.06.026 10.1177/030913330102500104 10.1016/j.jhydrol.2015.08.008 10.1016/j.isprsjprs.2017.11.009 10.1007/s11269-017-1796-1 10.1016/j.jhydrol.2013.05.038 10.1080/10962247.2018.1459956 10.5194/hess-22-6005-2018 10.1007/s11269-017-1649-y 10.1016/j.jhydrol.2013.11.021 10.1016/j.jcp.2018.04.018 10.1515/jwld-2016-0003 10.1162/neco.1997.9.8.1735 10.1007/s11263-017-1038-2 10.1016/j.neunet.2014.08.005 10.1016/j.jhydrol.2013.03.024 10.1002/met.1533 10.18653/v1/D16-1137 10.1016/j.jhydrol.2015.07.057 10.1016/j.marpolbul.2006.04.003 10.1016/j.envsoft.2007.10.001 10.1016/j.jhydrol.2014.06.013 10.1613/jair.1.11198 10.1016/j.jhydrol.2019.06.036 10.1007/s11269-015-1182-9 10.1002/hyp.9559 10.1016/j.jhydrol.2014.07.036 10.1016/j.jhydrol.2016.11.033 10.1016/S1005-8885(17)60243-7 10.1623/hysj.52.3.414 10.1007/s11263-017-1033-7 10.1109/ICASSP.2018.8462105 10.1016/j.aqpro.2015.02.133 10.1016/j.jhydrol.2016.01.056 10.1016/j.neucom.2016.12.038 10.1007/s00477-017-1400-5 10.1016/j.jhydrol.2018.04.065 10.1016/j.jclepro.2018.10.243 10.1016/j.jhydrol.2019.124434 10.1016/j.jhydrol.2019.02.051 10.1016/j.jhydrol.2018.01.015 10.1109/JSTSP.2017.2759726 |
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| Copyright | 2020 Elsevier B.V. |
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| References | Zhang, Zhu, Zhang, Ye, Yang (b0235) 2018; 561 Valipour (b0220) 2016; 23 Noori, Kalin (b0135) 2016; 533 Zhu, Xu, Yang, Hauptmann (b0245) 2017; 124 Lohani, Goel, Bhatia (b0120) 2014; 509 Zhu, Zabaras (b0250) 2018; 366 Bengio, Vinyals, Jaitly, Shazeer (b0025) 2015 Zhou, Chang, Chang, Kao, Wang (b0240) 2019; 209 Chau (b0050) 2006; 52 Nourani, Komasi (b0150) 2013; 490 Tsai, Abrahart, Mount, Chang (b0215) 2014; 28 Adikari, Y., Yoshitani, J. 2009. Global Trends in Water-Related Disasters: An Insight for Policymakers, International Centre for Water Hazard and Risk Management (ICHARM). The United Nations World Water Development Report 3, Tsukuba, Japan. Hochreiter, Schmidhuber (b0085) 1997; 9 Liu, Wang, Liu, Zeng, Liu, Alsaadi (b0115) 2017; 234 Chiu, C.C., Sainath, T.N., Wu, Y., Prabhavalkar, R., Nguyen, P., Chen, Z., Kannan, A., Weiss, R.J., Rao, K., Gonina, E., Jaitly, N., Li, B., Chorowski, J., Bacchiani, M., 2018. State-of-the-art speech recognition with sequence-to-sequence models. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4774–4778. Malinowski, Rohrbach, Fritz (b0125) 2017; 125 Humphrey, Gibbs, Dandy, Maier (b0090) 2016; 540 Nourani, Molajou, Uzelaltinbulat, Sadikoglu (b0165) 2019 Chang, Tsai (b0045) 2016; 535 Nourani, Elkiran, Abdullahi (b0170) 2020; 581 Taormina, Chau, Sivakumar (b0210) 2015; 529 Badrzadeh, Sarukkalige, Jayawardena (b0020) 2015; 529 Fengming, Shufang, Zhimin, Bo, Shiming, Mingming (b0075) 2017; 24 Chang, Chen, Lu, Huang, Chang (b0035) 2014; 517 Freeman, Taylor, Gharabaghi, Thé (b0080) 2018; 68 Audhkhasi, Rosenberg, Sethy, Ramabhadran, Kingsbury (b0015) 2017; 11 Shoaib, Shamseldin, Khan, Khan, Khan, Sultan, Melville (b0195) 2018; 32 Nourani, Baghanam, Adamowski, Kisi (b0145) 2014; 514 Chen, Chang, Chang (b0055) 2013; 497 Sezen, Bezak, Bai, Šraj (b0180) 2019; 576 Dawson, Wilby (b0070) 2001; 25 Sutskever, Vinyals, Le (b0200) 2014 Jeong, Park (b0095) 2019; 572 Le, Ho, Lee, Jung (b0110) 2019; 11 Tan, Lei, Wang, Wang, Wen, Ji, Kang (b0205) 2018; 567 Shenify, Danesh, Gocić, Taher, Wahab, Gani, Shamshirband, Petković (b0190) 2016; 30 Abrahart, Heppenstall, See (b0005) 2007; 52 Chang, Shen, Chang (b0040) 2014; 519 Kalteh, Hjorth, Berndtsson (b0100) 2008; 23 Nourani (b0140) 2017; 544 Venugopalan, Rohrbach, Donahue, Mooney, Darrell, Saenko (b0225) 2015 Chandwani, Vyas, Agrawal, Sharma (b0030) 2015; 4 Nourani, Sattari, Molajou (b0160) 2017; 31 Sainath, Kingsbury, Saon, Soltau, Mohamed, Dahl, Ramabhadran (b0175) 2015; 64 Shafaei, Adamowski, Fakheri-Fard, Dinpashoh, Adamowski (b0185) 2016; 28 Marmanis, Schindler, Wegner, Galliani, Datcu, Stilla (b0130) 2018; 135 Nourani, Partoviyan (b0155) 2018; 32 Costa-Jussa (b0065) 2018; 61 Wiseman, S., Rush, A.M., 2016. Sequence-to-sequence learning as beam-search optimization. arXiv 1606.02960v2. Kratzert, Klotz, Brenner, Schulz, Herrnegger (b0105) 2018; 22 Nourani (10.1016/j.jhydrol.2020.124631_b0160) 2017; 31 Shafaei (10.1016/j.jhydrol.2020.124631_b0185) 2016; 28 Chang (10.1016/j.jhydrol.2020.124631_b0035) 2014; 517 Audhkhasi (10.1016/j.jhydrol.2020.124631_b0015) 2017; 11 Malinowski (10.1016/j.jhydrol.2020.124631_b0125) 2017; 125 Jeong (10.1016/j.jhydrol.2020.124631_b0095) 2019; 572 Chau (10.1016/j.jhydrol.2020.124631_b0050) 2006; 52 Nourani (10.1016/j.jhydrol.2020.124631_b0140) 2017; 544 Chen (10.1016/j.jhydrol.2020.124631_b0055) 2013; 497 Le (10.1016/j.jhydrol.2020.124631_b0110) 2019; 11 Abrahart (10.1016/j.jhydrol.2020.124631_b0005) 2007; 52 Sezen (10.1016/j.jhydrol.2020.124631_b0180) 2019; 576 Tan (10.1016/j.jhydrol.2020.124631_b0205) 2018; 567 Chang (10.1016/j.jhydrol.2020.124631_b0045) 2016; 535 Valipour (10.1016/j.jhydrol.2020.124631_b0220) 2016; 23 Shenify (10.1016/j.jhydrol.2020.124631_b0190) 2016; 30 Nourani (10.1016/j.jhydrol.2020.124631_b0165) 2019 Hochreiter (10.1016/j.jhydrol.2020.124631_b0085) 1997; 9 10.1016/j.jhydrol.2020.124631_b0230 Marmanis (10.1016/j.jhydrol.2020.124631_b0130) 2018; 135 Fengming (10.1016/j.jhydrol.2020.124631_b0075) 2017; 24 Chandwani (10.1016/j.jhydrol.2020.124631_b0030) 2015; 4 Dawson (10.1016/j.jhydrol.2020.124631_b0070) 2001; 25 Kratzert (10.1016/j.jhydrol.2020.124631_b0105) 2018; 22 Bengio (10.1016/j.jhydrol.2020.124631_b0025) 2015 Venugopalan (10.1016/j.jhydrol.2020.124631_b0225) 2015 10.1016/j.jhydrol.2020.124631_b0060 Sutskever (10.1016/j.jhydrol.2020.124631_b0200) 2014 Shoaib (10.1016/j.jhydrol.2020.124631_b0195) 2018; 32 Noori (10.1016/j.jhydrol.2020.124631_b0135) 2016; 533 Nourani (10.1016/j.jhydrol.2020.124631_b0150) 2013; 490 Badrzadeh (10.1016/j.jhydrol.2020.124631_b0020) 2015; 529 Lohani (10.1016/j.jhydrol.2020.124631_b0120) 2014; 509 Tsai (10.1016/j.jhydrol.2020.124631_b0215) 2014; 28 Zhang (10.1016/j.jhydrol.2020.124631_b0235) 2018; 561 Liu (10.1016/j.jhydrol.2020.124631_b0115) 2017; 234 Freeman (10.1016/j.jhydrol.2020.124631_b0080) 2018; 68 Nourani (10.1016/j.jhydrol.2020.124631_b0145) 2014; 514 Nourani (10.1016/j.jhydrol.2020.124631_b0155) 2018; 32 Zhu (10.1016/j.jhydrol.2020.124631_b0245) 2017; 124 Taormina (10.1016/j.jhydrol.2020.124631_b0210) 2015; 529 Zhou (10.1016/j.jhydrol.2020.124631_b0240) 2019; 209 Humphrey (10.1016/j.jhydrol.2020.124631_b0090) 2016; 540 Kalteh (10.1016/j.jhydrol.2020.124631_b0100) 2008; 23 10.1016/j.jhydrol.2020.124631_b0010 Costa-Jussa (10.1016/j.jhydrol.2020.124631_b0065) 2018; 61 Nourani (10.1016/j.jhydrol.2020.124631_b0170) 2020; 581 Zhu (10.1016/j.jhydrol.2020.124631_b0250) 2018; 366 Chang (10.1016/j.jhydrol.2020.124631_b0040) 2014; 519 Sainath (10.1016/j.jhydrol.2020.124631_b0175) 2015; 64 |
| References_xml | – volume: 23 start-page: 835 year: 2008 end-page: 845 ident: b0100 article-title: Review of the self-organizing map (SOM) approach in water resources: analysis, modelling and application publication-title: Environ. Modell. Software – volume: 535 start-page: 256 year: 2016 end-page: 269 ident: b0045 article-title: A nonlinear spatio-temporal lumping of radar rainfall for modeling multi-step-ahead inflow forecasts by data-driven techniques publication-title: J. Hydrol. – volume: 209 start-page: 134 year: 2019 end-page: 145 ident: b0240 article-title: Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts publication-title: J. Cleaner Prod. – start-page: 4534 year: 2015 end-page: 4542 ident: b0225 article-title: Sequence to sequence-video to text publication-title: Proceedings of the IEEE International Conference on Computer Vision – volume: 529 start-page: 1788 year: 2015 end-page: 1797 ident: b0210 article-title: Neural network river forecasting through baseflow separation and binary-coded swarm optimization publication-title: J. Hydrol. – volume: 25 start-page: 80 year: 2001 end-page: 108 ident: b0070 article-title: Hydrological modelling using artificial neural networks publication-title: Prog. Phys. Geogr. – volume: 52 start-page: 414 year: 2007 end-page: 431 ident: b0005 article-title: Timing error correction procedure applied to neural network rainfall—runoff modelling publication-title: Hydrol. Sci. J. – volume: 32 start-page: 545 year: 2018 end-page: 562 ident: b0155 article-title: Hybrid denoising-jittering data pre-processing approach to enhance multi-step-ahead rainfall–runoff modeling publication-title: Stochastic Environ. Res. Risk Assess. – volume: 514 start-page: 358 year: 2014 end-page: 377 ident: b0145 article-title: Applications of hybrid wavelet–artificial intelligence models in hydrology: a review publication-title: J. Hydrol. – volume: 28 start-page: 27 year: 2016 end-page: 36 ident: b0185 article-title: A wavelet-SARIMA-ANN hybrid model for precipitation forecasting publication-title: J. Water Land Develop. – start-page: 1171 year: 2015 end-page: 1179 ident: b0025 article-title: Scheduled sampling for sequence prediction with recurrent neural networks publication-title: Adv. Neural Inf. Process. Syst. – reference: Chiu, C.C., Sainath, T.N., Wu, Y., Prabhavalkar, R., Nguyen, P., Chen, Z., Kannan, A., Weiss, R.J., Rao, K., Gonina, E., Jaitly, N., Li, B., Chorowski, J., Bacchiani, M., 2018. State-of-the-art speech recognition with sequence-to-sequence models. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4774–4778. – volume: 509 start-page: 25 year: 2014 end-page: 41 ident: b0120 article-title: Improving real time flood forecasting using fuzzy inference system publication-title: J. Hydrol. – volume: 572 start-page: 261 year: 2019 end-page: 273 ident: b0095 article-title: Comparative applications of data-driven models representing water table fluctuations publication-title: J. Hydrol. – volume: 529 start-page: 1633 year: 2015 end-page: 1643 ident: b0020 article-title: Hourly runoff forecasting for flood risk management: Application of various computational intelligence models publication-title: J. Hydrol. – reference: Adikari, Y., Yoshitani, J. 2009. Global Trends in Water-Related Disasters: An Insight for Policymakers, International Centre for Water Hazard and Risk Management (ICHARM). The United Nations World Water Development Report 3, Tsukuba, Japan. – volume: 561 start-page: 918 year: 2018 end-page: 929 ident: b0235 article-title: Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas publication-title: J. Hydrol. – volume: 234 start-page: 11 year: 2017 end-page: 26 ident: b0115 article-title: A survey of deep neural network architectures and their applications publication-title: Neurocomputing – volume: 68 start-page: 866 year: 2018 end-page: 886 ident: b0080 article-title: Forecasting air quality time series using deep learning publication-title: J. Air Waste Manag. Assoc. – volume: 4 start-page: 1054 year: 2015 end-page: 1061 ident: b0030 article-title: Soft computing approach for rainfall-runoff modelling: a review publication-title: Aquat. Procedia – volume: 64 start-page: 39 year: 2015 end-page: 48 ident: b0175 article-title: Deep convolutional neural networks for large-scale speech tasks publication-title: Neural Networks – volume: 581 year: 2020 ident: b0170 article-title: Multi-step ahead modeling of reference evapotranspiration using a multi-model approach publication-title: J. Hydrol. – volume: 576 start-page: 98 year: 2019 end-page: 110 ident: b0180 article-title: Hydrological modelling of karst catchment using lumped conceptual and data mining models publication-title: J. Hydrol. – volume: 28 start-page: 1055 year: 2014 end-page: 1070 ident: b0215 article-title: Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan publication-title: Hydrol. Process. – reference: Wiseman, S., Rush, A.M., 2016. Sequence-to-sequence learning as beam-search optimization. arXiv 1606.02960v2. – volume: 366 start-page: 415 year: 2018 end-page: 447 ident: b0250 article-title: Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification publication-title: J. Comput. Phys. – volume: 497 start-page: 71 year: 2013 end-page: 79 ident: b0055 article-title: Reinforced recurrent neural networks for multi-step-ahead flood forecasts publication-title: J. Hydrol. – volume: 11 start-page: 1387 year: 2019 ident: b0110 article-title: Application of long short-term memory (LSTM) neural network for flood forecasting publication-title: Water – start-page: 3104 year: 2014 end-page: 3112 ident: b0200 article-title: Sequence to sequence learning with neural networks publication-title: Adv. Neural Inf. Process. Syst. – volume: 32 start-page: 83 year: 2018 end-page: 103 ident: b0195 article-title: A comparative study of various hybrid wavelet feedforward neural network models for runoff forecasting publication-title: Water Resour. Manage. – volume: 519 start-page: 476 year: 2014 end-page: 489 ident: b0040 article-title: Regional flood inundation nowcast using hybrid SOM and dynamic neural networks publication-title: J. Hydrol. – start-page: 1 year: 2019 end-page: 16 ident: b0165 article-title: Emotional artificial neural networks (EANNs) for multi-step ahead prediction of monthly precipitation; case study: northern Cyprus publication-title: Theor. Appl. Climatol. – volume: 540 start-page: 623 year: 2016 end-page: 640 ident: b0090 article-title: A hybrid approach to monthly streamflow forecasting: integrating hydrological model outputs into a Bayesian artificial neural network publication-title: J. Hydrol. – volume: 30 start-page: 641 year: 2016 end-page: 652 ident: b0190 article-title: Precipitation estimation using support vector machine with discrete wavelet transform publication-title: Water Resour. Manage. – volume: 61 start-page: 947 year: 2018 end-page: 974 ident: b0065 article-title: From feature to paradigm: deep learning in machine translation publication-title: J. Artificial Intelligence Res. – volume: 490 start-page: 41 year: 2013 end-page: 55 ident: b0150 article-title: A geomorphology-based ANFIS model for multi-station modeling of rainfall–runoff process publication-title: J. Hydrol. – volume: 125 start-page: 110 year: 2017 end-page: 135 ident: b0125 article-title: Ask your neurons: a deep learning approach to visual question answering publication-title: Int. J. Comput. Vision – volume: 24 start-page: 67 year: 2017 end-page: 73 ident: b0075 article-title: Anomaly detection in smart grid based on encoder-decoder framework with recurrent neural network publication-title: J. China Univ. Posts Telecommun. – volume: 135 start-page: 158 year: 2018 end-page: 172 ident: b0130 article-title: Classification with an edge: improving semantic image segmentation with boundary detection publication-title: ISPRS J. Photogramm. Remote Sens. – volume: 52 start-page: 726 year: 2006 end-page: 733 ident: b0050 article-title: A review on integration of artificial intelligence into water quality modelling publication-title: Mar. Pollut. Bull. – volume: 517 start-page: 836 year: 2014 end-page: 846 ident: b0035 article-title: Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control publication-title: J. Hydrol. – volume: 533 start-page: 141 year: 2016 end-page: 151 ident: b0135 article-title: Coupling SWAT and ANN models for enhanced daily streamflow prediction publication-title: J. Hydrol. – volume: 124 start-page: 409 year: 2017 end-page: 421 ident: b0245 article-title: Uncovering the temporal context for video question answering publication-title: Int. J. Comput. Vision – volume: 23 start-page: 91 year: 2016 end-page: 100 ident: b0220 article-title: Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms publication-title: Meteorol. Appl. – volume: 31 start-page: 2645 year: 2017 end-page: 2658 ident: b0160 article-title: Threshold-based hybrid data mining method for long-term maximum precipitation forecasting publication-title: Water Resour. Manage. – volume: 11 start-page: 1351 year: 2017 end-page: 1359 ident: b0015 article-title: End-to-end ASR-free keyword search from speech publication-title: IEEE J. Sel. Top. Signal Process. – volume: 22 start-page: 6005 year: 2018 end-page: 6022 ident: b0105 article-title: Rainfall–runoff modelling using long short-term memory (LSTM) networks publication-title: Hydrol. Earth Syst. Sci. – volume: 567 start-page: 767 year: 2018 end-page: 780 ident: b0205 article-title: An adaptive middle and long-term runoff forecast model using EEMD-ANN hybrid approach publication-title: J. Hydrol. – volume: 544 start-page: 267 year: 2017 end-page: 277 ident: b0140 article-title: An emotional ANN (EANN) approach to modeling rainfall-runoff process publication-title: J. Hydrol. – volume: 9 start-page: 1735 year: 1997 end-page: 1780 ident: b0085 article-title: Long short-term memory publication-title: Neural Comput. – volume: 533 start-page: 141 year: 2016 ident: 10.1016/j.jhydrol.2020.124631_b0135 article-title: Coupling SWAT and ANN models for enhanced daily streamflow prediction publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2015.11.050 – volume: 11 start-page: 1387 issue: 7 year: 2019 ident: 10.1016/j.jhydrol.2020.124631_b0110 article-title: Application of long short-term memory (LSTM) neural network for flood forecasting publication-title: Water doi: 10.3390/w11071387 – volume: 514 start-page: 358 year: 2014 ident: 10.1016/j.jhydrol.2020.124631_b0145 article-title: Applications of hybrid wavelet–artificial intelligence models in hydrology: a review publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2014.03.057 – volume: 540 start-page: 623 year: 2016 ident: 10.1016/j.jhydrol.2020.124631_b0090 article-title: A hybrid approach to monthly streamflow forecasting: integrating hydrological model outputs into a Bayesian artificial neural network publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2016.06.026 – volume: 25 start-page: 80 issue: 1 year: 2001 ident: 10.1016/j.jhydrol.2020.124631_b0070 article-title: Hydrological modelling using artificial neural networks publication-title: Prog. Phys. Geogr. doi: 10.1177/030913330102500104 – volume: 529 start-page: 1788 year: 2015 ident: 10.1016/j.jhydrol.2020.124631_b0210 article-title: Neural network river forecasting through baseflow separation and binary-coded swarm optimization publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2015.08.008 – volume: 135 start-page: 158 year: 2018 ident: 10.1016/j.jhydrol.2020.124631_b0130 article-title: Classification with an edge: improving semantic image segmentation with boundary detection publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2017.11.009 – volume: 32 start-page: 83 issue: 1 year: 2018 ident: 10.1016/j.jhydrol.2020.124631_b0195 article-title: A comparative study of various hybrid wavelet feedforward neural network models for runoff forecasting publication-title: Water Resour. Manage. doi: 10.1007/s11269-017-1796-1 – volume: 497 start-page: 71 year: 2013 ident: 10.1016/j.jhydrol.2020.124631_b0055 article-title: Reinforced recurrent neural networks for multi-step-ahead flood forecasts publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2013.05.038 – volume: 68 start-page: 866 issue: 8 year: 2018 ident: 10.1016/j.jhydrol.2020.124631_b0080 article-title: Forecasting air quality time series using deep learning publication-title: J. Air Waste Manag. Assoc. doi: 10.1080/10962247.2018.1459956 – volume: 22 start-page: 6005 issue: 11 year: 2018 ident: 10.1016/j.jhydrol.2020.124631_b0105 article-title: Rainfall–runoff modelling using long short-term memory (LSTM) networks publication-title: Hydrol. Earth Syst. Sci. doi: 10.5194/hess-22-6005-2018 – volume: 31 start-page: 2645 issue: 9 year: 2017 ident: 10.1016/j.jhydrol.2020.124631_b0160 article-title: Threshold-based hybrid data mining method for long-term maximum precipitation forecasting publication-title: Water Resour. Manage. doi: 10.1007/s11269-017-1649-y – volume: 509 start-page: 25 year: 2014 ident: 10.1016/j.jhydrol.2020.124631_b0120 article-title: Improving real time flood forecasting using fuzzy inference system publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2013.11.021 – volume: 366 start-page: 415 year: 2018 ident: 10.1016/j.jhydrol.2020.124631_b0250 article-title: Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2018.04.018 – volume: 28 start-page: 27 issue: 1 year: 2016 ident: 10.1016/j.jhydrol.2020.124631_b0185 article-title: A wavelet-SARIMA-ANN hybrid model for precipitation forecasting publication-title: J. Water Land Develop. doi: 10.1515/jwld-2016-0003 – start-page: 4534 year: 2015 ident: 10.1016/j.jhydrol.2020.124631_b0225 article-title: Sequence to sequence-video to text – volume: 9 start-page: 1735 issue: 8 year: 1997 ident: 10.1016/j.jhydrol.2020.124631_b0085 article-title: Long short-term memory publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – volume: 125 start-page: 110 issue: 1–3 year: 2017 ident: 10.1016/j.jhydrol.2020.124631_b0125 article-title: Ask your neurons: a deep learning approach to visual question answering publication-title: Int. J. Comput. Vision doi: 10.1007/s11263-017-1038-2 – volume: 64 start-page: 39 year: 2015 ident: 10.1016/j.jhydrol.2020.124631_b0175 article-title: Deep convolutional neural networks for large-scale speech tasks publication-title: Neural Networks doi: 10.1016/j.neunet.2014.08.005 – volume: 490 start-page: 41 year: 2013 ident: 10.1016/j.jhydrol.2020.124631_b0150 article-title: A geomorphology-based ANFIS model for multi-station modeling of rainfall–runoff process publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2013.03.024 – volume: 23 start-page: 91 issue: 1 year: 2016 ident: 10.1016/j.jhydrol.2020.124631_b0220 article-title: Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms publication-title: Meteorol. Appl. doi: 10.1002/met.1533 – ident: 10.1016/j.jhydrol.2020.124631_b0230 doi: 10.18653/v1/D16-1137 – volume: 529 start-page: 1633 year: 2015 ident: 10.1016/j.jhydrol.2020.124631_b0020 article-title: Hourly runoff forecasting for flood risk management: Application of various computational intelligence models publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2015.07.057 – start-page: 1171 year: 2015 ident: 10.1016/j.jhydrol.2020.124631_b0025 article-title: Scheduled sampling for sequence prediction with recurrent neural networks publication-title: Adv. Neural Inf. Process. Syst. – volume: 52 start-page: 726 issue: 7 year: 2006 ident: 10.1016/j.jhydrol.2020.124631_b0050 article-title: A review on integration of artificial intelligence into water quality modelling publication-title: Mar. Pollut. Bull. doi: 10.1016/j.marpolbul.2006.04.003 – volume: 23 start-page: 835 issue: 7 year: 2008 ident: 10.1016/j.jhydrol.2020.124631_b0100 article-title: Review of the self-organizing map (SOM) approach in water resources: analysis, modelling and application publication-title: Environ. Modell. Software doi: 10.1016/j.envsoft.2007.10.001 – volume: 517 start-page: 836 year: 2014 ident: 10.1016/j.jhydrol.2020.124631_b0035 article-title: Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2014.06.013 – start-page: 3104 year: 2014 ident: 10.1016/j.jhydrol.2020.124631_b0200 article-title: Sequence to sequence learning with neural networks publication-title: Adv. Neural Inf. Process. Syst. – volume: 61 start-page: 947 year: 2018 ident: 10.1016/j.jhydrol.2020.124631_b0065 article-title: From feature to paradigm: deep learning in machine translation publication-title: J. Artificial Intelligence Res. doi: 10.1613/jair.1.11198 – volume: 576 start-page: 98 year: 2019 ident: 10.1016/j.jhydrol.2020.124631_b0180 article-title: Hydrological modelling of karst catchment using lumped conceptual and data mining models publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2019.06.036 – volume: 30 start-page: 641 issue: 2 year: 2016 ident: 10.1016/j.jhydrol.2020.124631_b0190 article-title: Precipitation estimation using support vector machine with discrete wavelet transform publication-title: Water Resour. Manage. doi: 10.1007/s11269-015-1182-9 – volume: 28 start-page: 1055 issue: 3 year: 2014 ident: 10.1016/j.jhydrol.2020.124631_b0215 article-title: Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan publication-title: Hydrol. Process. doi: 10.1002/hyp.9559 – ident: 10.1016/j.jhydrol.2020.124631_b0010 – volume: 519 start-page: 476 year: 2014 ident: 10.1016/j.jhydrol.2020.124631_b0040 article-title: Regional flood inundation nowcast using hybrid SOM and dynamic neural networks publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2014.07.036 – volume: 544 start-page: 267 year: 2017 ident: 10.1016/j.jhydrol.2020.124631_b0140 article-title: An emotional ANN (EANN) approach to modeling rainfall-runoff process publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2016.11.033 – volume: 24 start-page: 67 issue: 6 year: 2017 ident: 10.1016/j.jhydrol.2020.124631_b0075 article-title: Anomaly detection in smart grid based on encoder-decoder framework with recurrent neural network publication-title: J. China Univ. Posts Telecommun. doi: 10.1016/S1005-8885(17)60243-7 – volume: 52 start-page: 414 issue: 3 year: 2007 ident: 10.1016/j.jhydrol.2020.124631_b0005 article-title: Timing error correction procedure applied to neural network rainfall—runoff modelling publication-title: Hydrol. Sci. J. doi: 10.1623/hysj.52.3.414 – volume: 124 start-page: 409 issue: 3 year: 2017 ident: 10.1016/j.jhydrol.2020.124631_b0245 article-title: Uncovering the temporal context for video question answering publication-title: Int. J. Comput. Vision doi: 10.1007/s11263-017-1033-7 – ident: 10.1016/j.jhydrol.2020.124631_b0060 doi: 10.1109/ICASSP.2018.8462105 – volume: 4 start-page: 1054 year: 2015 ident: 10.1016/j.jhydrol.2020.124631_b0030 article-title: Soft computing approach for rainfall-runoff modelling: a review publication-title: Aquat. Procedia doi: 10.1016/j.aqpro.2015.02.133 – start-page: 1 year: 2019 ident: 10.1016/j.jhydrol.2020.124631_b0165 article-title: Emotional artificial neural networks (EANNs) for multi-step ahead prediction of monthly precipitation; case study: northern Cyprus publication-title: Theor. Appl. Climatol. – volume: 535 start-page: 256 year: 2016 ident: 10.1016/j.jhydrol.2020.124631_b0045 article-title: A nonlinear spatio-temporal lumping of radar rainfall for modeling multi-step-ahead inflow forecasts by data-driven techniques publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2016.01.056 – volume: 234 start-page: 11 year: 2017 ident: 10.1016/j.jhydrol.2020.124631_b0115 article-title: A survey of deep neural network architectures and their applications publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.12.038 – volume: 32 start-page: 545 issue: 2 year: 2018 ident: 10.1016/j.jhydrol.2020.124631_b0155 article-title: Hybrid denoising-jittering data pre-processing approach to enhance multi-step-ahead rainfall–runoff modeling publication-title: Stochastic Environ. Res. Risk Assess. doi: 10.1007/s00477-017-1400-5 – volume: 561 start-page: 918 year: 2018 ident: 10.1016/j.jhydrol.2020.124631_b0235 article-title: Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2018.04.065 – volume: 209 start-page: 134 year: 2019 ident: 10.1016/j.jhydrol.2020.124631_b0240 article-title: Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts publication-title: J. Cleaner Prod. doi: 10.1016/j.jclepro.2018.10.243 – volume: 581 year: 2020 ident: 10.1016/j.jhydrol.2020.124631_b0170 article-title: Multi-step ahead modeling of reference evapotranspiration using a multi-model approach publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2019.124434 – volume: 572 start-page: 261 year: 2019 ident: 10.1016/j.jhydrol.2020.124631_b0095 article-title: Comparative applications of data-driven models representing water table fluctuations publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2019.02.051 – volume: 567 start-page: 767 year: 2018 ident: 10.1016/j.jhydrol.2020.124631_b0205 article-title: An adaptive middle and long-term runoff forecast model using EEMD-ANN hybrid approach publication-title: J. Hydrol. doi: 10.1016/j.jhydrol.2018.01.015 – volume: 11 start-page: 1351 issue: 8 year: 2017 ident: 10.1016/j.jhydrol.2020.124631_b0015 article-title: End-to-end ASR-free keyword search from speech publication-title: IEEE J. Sel. Top. Signal Process. doi: 10.1109/JSTSP.2017.2759726 |
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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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