The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting
Open accessAbstract
Flood forecasting is an essential requirement in integrated water resource management. This paper suggests a Long Short-Term Memory (LSTM) neural network model for flood forecasting, where the daily discharge and rainfall were used as input data. Moreover, characteristics of the data sets which may influence the model performance were also of interest. As a result, the Da River basin in Vietnam was chosen and two different combinations of input data sets from before 1985 (when the Hoa Binh dam was built) were used for one-day, two-day, and three-day flowrate forecasting ahead at Hoa Binh Station. The predictive ability of the model is quite impressive: The Nash–Sutcliffe efficiency (NSE) reached 99%, 95%, and 87% corresponding to three forecasting cases, respectively. The findings of this study suggest a viable option for flood forecasting on the Da River in Vietnam, where the river basin stretches between many countries and downstream flows (Vietnam) may fluctuate suddenly due to flood discharge from upstream hydroelectric reservoirs.
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.1078 · Cited by 3,317
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Stock market's price movement prediction with LSTM neural networks
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“for time sequence problems) such as: Speech recognition [19], machine translation [20,21], language modeling [22], tourism field [23,24], stock prediction [25], and rainfall-runoff simulation [16,26,27].”
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Cited by
Feng, D., Fang, K., & Shen, C. (2020) · Water Resources Research · doi:10.1029/2019wr026793 · Cited by 633
Long lead-time daily and monthly streamflow forecasting using machine learning methods
Cheng, M., Fang, F., Kinouchi, T., Navon, I. M., & Pain, C. C. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.125376 · Cited by 312
Lv, Z., & Xie, S. (2024) · Digital Twin · doi:10.12688/digitaltwin.17524.2 · Cited by 112
Younis, H. A., Ruhaiyem, N. I. R., Badr, A. A., Abdul-Hassan, A. K., Alfadli, I. M., Binjumah, W. M., Altuwaijri, E. A., & Nasser, M. (2023) · Processes · doi:10.3390/pr11051488 · Cited by 21
Metadata
- Authors
- Le, X.-H., Ho, H. V., Lee, G., & Jung, S.
- Venue
- Water
- Publisher
- MDPI AG
- Published
- 2019-07-05
- Volume
- 11
- Issue
- 7
- Pages
- 1387
- ISSN
- 2073-4441 (ISSN-L)
- OpenAlex
- W2953521532
- Cited by
- 889
- References
- 9
- FWCI
- 38.66
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
