The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.
Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq
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Citations
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
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
Extreme Learning Machine for Regression and Multiclass Classification
Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2011.2168604 · Cited by 5,550
Fast Training of Support Vector Machines Using Sequential Minimal Optimization
Platt, J. C. (1998) · Advances in Kernel Methods · doi:10.7551/mitpress/1130.003.0016 · Cited by 5,437
A Practical Guide to Support Vector Classication
Hsu, C., Chang, C.-C., & Lin, C. (2008) · Cited by 5,046
Willmott, C. J. (1981) · Physical Geography · doi:10.1080/02723646.1981.10642213 · Cited by 4,916
Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation
Legates, D. R., & McCabe, G. J. (1999) · Water Resources Research · doi:10.1029/1998wr900018 · Cited by 4,591
Forecasting with artificial neural networks:
Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998) · International Journal of Forecasting · doi:10.1016/s0169-2070(97)00044-7 · Cited by 4,229
Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes
Huang, G.-B., Chen, L., & Siew, C.-K. (2006) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2006.875977 · Cited by 2,614
Cited by
Sharma, R., Kamble, S. S., Gunasekaran, A., Kumar, V., & Kumar, A. (2020) · Computers & Operations Research · doi:10.1016/j.cor.2020.104926 · Cited by 766
Predicting compressive strength of lightweight foamed concrete using extreme learning machine model
Yaseen, Z. M., Deo, R. C., Hilal, A., Abd, A. M., Bueno, L. C., Salcedo-Sanz, S., & Nehdi, M. L. (2018) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2017.09.004 · Cited by 434
Self-citationStatistical downscaling of precipitation using machine learning techniques
Sachindra, D. A., Ahmed, K., Rashid, M. M., Shahid, S., & Perera, B. J. C. (2018) · Atmospheric Research · doi:10.1016/j.atmosres.2018.05.022 · Cited by 373
Sahoo, B. B., Jha, R., Singh, A., & Kumar, D. (2019) · Acta Geophysica · doi:10.1007/s11600-019-00330-1 · Cited by 339
Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model
Yaseen, Z. M., Ebtehaj, I., Bonakdari, H., Deo, R. C., Danandeh Mehr, A., Mohtar, W. H. M. W., Diop, L., El-shafie, A., & Singh, V. P. (2017) · Journal of Hydrology · doi:10.1016/j.jhydrol.2017.09.007 · Cited by 267
Self-citation
Metadata
- Authors
- Yaseen, Z. M., Jaafar, O., Deo, R. C., Kisi, O., Adamowski, J., Quilty, J., & El-Shafie, A.
- Venue
- Journal of Hydrology
- Publisher
- Elsevier BV
- Published
- 2016-11
- Volume
- 542
- Pages
- 603-614
- ISSN
- 0022-1694 (ISSN-L)
- OpenAlex
- W2518675717
- Cited by
- 329
- References
- 78
- FWCI
- 19.43
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq. https://app.cochat.ai/papers/10.1016/j.jhydrol.2016.09.035
- The paper
- Yaseen, Z. M., Jaafar, O., Deo, R. C., Kisi, O., Adamowski, J., Quilty, J., & El-Shafie, A. (2016). Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2016.09.035
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.
