The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Crop evapotranspiration : guidelines for computing crop water requirements
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998) · FAO eBooks · Cited by 20,520
Extreme learning machine: Theory and applications
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.126 · Cited by 13,285
Reference Crop Evapotranspiration from Temperature
Hargreaves, G. H., & Samani, Z. A. (1985) · Applied Engineering in Agriculture · doi:10.13031/2013.26773 · Cited by 4,574
A general regression neural network
Specht, D. F. (1991) · IEEE Transactions on Neural Networks · doi:10.1109/72.97934 · Cited by 4,492
Estimating Reference Evapotranspiration Under Inaccurate Data Conditions
Droogers, P., & Allen, R. G. (2002) · Irrigation and Drainage Systems · doi:10.1023/a:1015508322413 · Cited by 1,018
A review of approaches for evapotranspiration partitioning
Kool, D., Agam, N., Lazarovitch, N., Heitman, J. L., Sauer, T. J., & Ben-Gal, A. (2014) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2013.09.003 · Cited by 824
Estimating Evapotranspiration using Artificial Neural Network
Kumar, M., Raghuwanshi, N. S., Singh, R., Wallender, W. W., & Pruitt, W. O. (2002) · Journal of Irrigation and Drainage Engineering · doi:10.1061/(asce)0733-9437(2002)128:4(224 · Cited by 529
Xu, C.-Y., & Singh, V. P. (2002) · Water Resources Management · doi:10.1023/a:1020282515975 · Cited by 342
Xu, C., & Singh, V. P. (2004) · Journal of Hydrology · doi:10.1016/j.jhydrol.2004.10.024 · Cited by 329
Tabari, H., Kisi, O., Ezani, A., & Hosseinzadeh Talaee, P. (2012) · Journal of Hydrology · doi:10.1016/j.jhydrol.2012.04.007 · Cited by 311
Cited by
Machine Learning in Agriculture: A Review
Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018) · Sensors · doi:10.3390/s18082674 · Cited by 3,441
Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions
Huang, G., Wu, L., Ma, X., Zhang, W., Fan, J., Yu, X., Zeng, W., & Zhou, H. (2019) · Journal of Hydrology · doi:10.1016/j.jhydrol.2019.04.085 · Cited by 560
Evapotranspiration evaluation models based on machine learning algorithms—A comparative study
Granata, F. (2019) · Agricultural Water Management · doi:10.1016/j.agwat.2019.03.015 · Cited by 361
Physics‐Constrained Machine Learning of Evapotranspiration
Zhao, W. L., Gentine, P., Reichstein, M., Zhang, Y., Zhou, S., Wen, Y., Lin, C., Li, X., & Qiu, G. Y. (2019) · Geophysical Research Letters · doi:10.1029/2019gl085291 · Cited by 315
Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture
Mekonnen, Y., Namuduri, S., Burton, L., Sarwat, A., & Bhansali, S. (2020) · Journal of The Electrochemical Society · doi:10.1149/2.0222003jes · Cited by 282
Metadata
- Authors
- Feng, Y., Peng, Y., Cui, N., Gong, D., & Zhang, K.
- Venue
- Computers and Electronics in Agriculture
- Publisher
- Elsevier BV
- Published
- 2017-04
- Volume
- 136
- Pages
- 71-78
- ISSN
- 0168-1699 (ISSN-L)
- OpenAlex
- W2603417106
- Cited by
- 265
- References
- 26
- FWCI
- 12.65
- 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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data. https://app.cochat.ai/papers/10.1016/j.compag.2017.01.027
- The paper
- Feng, Y., Peng, Y., Cui, N., Gong, D., & Zhang, K. (2017). Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2017.01.027
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.
