The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models
Overview
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Citations
Cites
Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879
Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
Cited by
Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals
Li, C., Sanchez, R.-V., Zurita, G., Cerrada, M., Cabrera, D., & Vásquez, R. E. (2016) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2016.02.007 · Cited by 470
Self-citationLi, W., & Hsu, C.-Y. (2022) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11070385 · Cited by 147
Metadata
- Authors
- Bai, Y., Chen, Z., Xie, J., & Li, C.
- Venue
- Journal of Hydrology
- Publisher
- Elsevier BV
- Published
- 2016-01
- Volume
- 532
- Pages
- 193-206
- ISSN
- 0022-1694 (ISSN-L)
- OpenAlex
- W2181125631
- Cited by
- 199
- References
- 2
- FWCI
- 10.36
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models. https://app.cochat.ai/papers/10.1016/j.jhydrol.2015.11.011
- The paper
- Bai, Y., Chen, Z., Xie, J., & Li, C. (2016). Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2015.11.011
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.
