The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting
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Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
River flow forecasting through conceptual models part I — A discussion of principles
Nash, J. E., & Sutcliffe, J. V. (1970) · Journal of Hydrology · doi:10.1016/0022-1694(70)90255-6 · Cited by 23,674
Interpretation of the correlation coefficient: A basic review
Taylor, R. (1990) · Journal of Diagnostic Medical Sonography · doi:10.1177/875647939000600106 · Cited by 2,390
Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks
(2018) · doi:10.5194/hess-22-6005-2018 · Cited by 2,114
Flood Prediction Using Machine Learning Models: Literature Review
Mosavi, A., Ozturk, P., & Chau, K.-W. (2018) · Water · doi:10.3390/w10111536 · Cited by 1,672
Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting
Le, X.-H., Ho, H. V., Lee, G., & Jung, S. (2019) · Water · doi:10.3390/w11071387 · Cited by 889
Deep Learning for Spatio-Temporal Data Mining: A Survey
Wang, S., Cao, J., & Yu, P. S. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3025580 · Cited by 822
Zhang, J., Zhu, Y., Zhang, X., Ye, M., & Yang, J. (2018) · Journal of Hydrology · doi:10.1016/j.jhydrol.2018.04.065 · Cited by 816
A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning
Xiang, Z., Yan, J., & Demir, I. (2020) · Water Resources Research · doi:10.1029/2019wr025326 · Cited by 768
Metadata
- Authors
- Rahimzad, M., Moghaddam Nia, A., Zolfonoon, H., Soltani, J., Danandeh Mehr, A., & Kwon, H.-H.
- Venue
- Water Resources Management
- Published
- 2021-08-16
- Volume
- 35
- Issue
- 12
- Pages
- 4167-4187
- ISSN
- 0920-4741 (ISSN-L)
- 1573-1650
- OpenAlex
- W3194796466
- Cited by
- 266
- References
- 58
- FWCI
- 11.97
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting. https://app.cochat.ai/papers/10.1007/s11269-021-02937-w
- The paper
- Rahimzad, M., Moghaddam Nia, A., Zolfonoon, H., Soltani, J., Danandeh Mehr, A., & Kwon, H.-H. (2021). Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting. Water Resources Management. https://doi.org/10.1007/s11269-021-02937-w
Data sources
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.
