The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Time Series Analysis: Forecasting and Control
Geurts, M., Box, G. E. P., & Jenkins, G. M. (1977) · Journal of Marketing Research · doi:10.2307/3150485 · Cited by 19,186
Time Series Analysis Forecasting and Control.
Bartholomew, D. J., Box, G. E. P., & Jenkins, G. M. (1971) · Operational Research Quarterly (1970-1977) · doi:10.2307/3008255 · Cited by 6,716
Recurrent neural network based language model
Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390
Forecasting principles and practice
Hyndman, R. J., & Athanasopoulos, G. (2018) · Cited by 4,405
State-of-the-art in artificial neural network applications: A survey
Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018) · Heliyon · doi:10.1016/j.heliyon.2018.e00938 · Cited by 3,054
Learning to forget: continual prediction with LSTM
Gers, F. A. (1999) · 9th International Conference on Artificial Neural Networks: ICANN '99 · doi:10.1049/cp:19991218 · Cited by 2,337
Rainfall‐Runoff Modelling: The Primer
Beven, K. (2012) · Wiley eBooks · doi:10.1002/9781119951001 · Cited by 2,051
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Cited by
Deep learning modelling techniques: Current progress, applications, advantages, and challenges
Ahmed, S. F., Alam, M. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A. B. M., & Gandomi, A. H. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10466-8 · Cited by 1,079
Forecast Methods for Time Series Data: A Survey
Liu, Z., Zhu, Z., Gao, J., & Xu, C. (2021) · IEEE Access · doi:10.1109/access.2021.3091162 · Cited by 247
Materia, S., García, L. P., van Straaten, C., O, S., Mamalakis, A., Cavicchia, L., Coumou, D., de Luca, P., Kretschmer, M., & Donat, M. (2024) · WIREs Climate Change · doi:10.1002/wcc.914 · Cited by 130
A Data-driven Control Policy based Driving Safety Analysis System for Autonomous Vehicles
Kang, L., Shen, H., Li, Y., & Xu, S. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3244756 · Cited by 12
Metadata
- Authors
- Sahoo, B. B., Jha, R., Singh, A., & Kumar, D.
- Venue
- Acta Geophysica
- Published
- 2019-07-20
- Volume
- 67
- Issue
- 5
- Pages
- 1471-1481
- ISSN
- 0001-5725 (ISSN-L)
- 1895-6572
- 1895-7455
- OpenAlex
- W2964006806
- Cited by
- 339
- References
- 61
- FWCI
- 12.13
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting. https://app.cochat.ai/papers/10.1007/s11600-019-00330-1
- The paper
- Sahoo, B. B., Jha, R., Singh, A., & Kumar, D. (2019). Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting. Acta Geophysica. https://doi.org/10.1007/s11600-019-00330-1
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
