The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Water quality prediction using machine learning methods
Abstract
This study investigates the performance of artificial intelligence techniques including artificial neural network (ANN), group method of data handling (GMDH) and support vector machine (SVM) for predicting water quality components of Tireh River located in the southwest of Iran. To develop the ANN and SVM, different types of transfer and kernel functions were tested, respectively. Reviewing the results of ANN and SVM indicated that both models have suitable performance for predicting water quality components. During the process of development of ANN and SVM, it was found that tansig and RBF as transfer and kernel functions have the best performance among the tested functions. Comparison of outcomes of GMDH model with other applied models shows that although this model has acceptable performance for predicting the components of water quality, its accuracy is slightly less than ANN and SVM. The evaluation of the accuracy of the applied models according to the error indexes declared that SVM was the most accurate model. Examining the results of the models showed that all of them had some over-estimation properties. By evaluating the results of the models based on the DDR index, it was found that the lowest DDR value was related to the performance of the SVM model.
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
Polynomial Theory of Complex Systems
Ivakhnenko, A. G. (1971) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1971.4308320 · Cited by 1,684
Evaluation of water quality using water quality index (WQI) method and GIS in Aksu River (SW-Turkey)
Şener, Ş., Şener, E., & Davraz, A. (2017) · Science of The Total Environment · doi:10.1016/j.scitotenv.2017.01.102 · Cited by 643
Wu, W. (2007) · CRC Press eBooks · doi:10.4324/9780203938485 · Cited by 540
An ANN application for water quality forecasting
Palani, S., Liong, S.-Y., & Tkalich, P. (2008) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2008.05.021 · Cited by 525
Noori, R., Karbassi, A. R., Moghaddamnia, A., Han, D., Zokaei-Ashtiani, M. H., Farokhnia, A., & Gousheh, M. G. (2011) · Journal of Hydrology · doi:10.1016/j.jhydrol.2011.02.021 · Cited by 371
Motagh, M., Shamshiri, R., Haghighi, M. H., Wetzel, H., Akbari, B., Nahavandchi, H., Roessner, S., & Arabi, S. (2017) · Engineering Geology · doi:10.1016/j.enggeo.2017.01.011 · Cited by 279
Handbook of Genetic Programming Applications
(2015) · Springer eBooks · doi:10.1007/978-3-319-20883-1 · Cited by 226
Emamgholizadeh, S., Kashi, H., Marofpoor, I., & Zalaghi, E. (2014) · International Journal of Environmental Science and Technology · doi:10.1007/s13762-013-0378-x · Cited by 162
Metaheuristics in Water, Geotechnical and Transport Engineering
(2013) · Elsevier eBooks · doi:10.1016/c2011-0-07801-8 · Cited by 157
Prediction of side weir discharge coefficient by support vector machine technique
Azamathulla, H. M., Haghiabi, A. H., & Parsaie, A. (2016) · Water Science & Technology Water Supply · doi:10.2166/ws.2016.014 · Cited by 151
Self-citation
Cited by
A survey on river water quality modelling using artificial intelligence models: 2000–2020
Tiyasha, Tung, T. M., & Yaseen, Z. M. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.124670 · Cited by 650
Application of support vector regression for modeling low flow time series
Sahoo, B. B., Jha, R., Singh, A., & Kumar, D. (2019) · KSCE Journal of Civil Engineering · doi:10.1007/s12205-018-0128-1 · Cited by 56
Chhipi-Shrestha, G., Mian, H. R., Mohammadiun, S., Rodriguez, M., Hewage, K., & Sadiq, R. (2023) · Clean Technologies and Environmental Policy · doi:10.1007/s10098-023-02477-4 · Cited by 46
Metadata
- Authors
- Haghiabi, A. H., Nasrolahi, A. H., & Parsaie, A.
- Venue
- Water Quality Research Journal
- Publisher
- IWA Publishing
- Published
- 2018-01-19
- Volume
- 53
- Issue
- 1
- Pages
- 3-13
- ISSN
- 1201-3080 (ISSN-L)
- 2408-9443
- OpenAlex
- W2785103246
- Cited by
- 445
- References
- 26
- FWCI
- 11.72
- 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: Water quality prediction using machine learning methods. https://app.cochat.ai/papers/10.2166/wqrj.2018.025
- The paper
- Haghiabi, A. H., Nasrolahi, A. H., & Parsaie, A. (2018). Water quality prediction using machine learning methods. Water Quality Research Journal. https://doi.org/10.2166/wqrj.2018.025
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.
