Performance assessment of artificial neural networks and support vector regression models for stream flow predictions-Reference-Cited by-同舟云学术

Performance assessment of artificial neural networks and support vector regression models for stream flow predictions

Published:2018-11-08 Issue:12 Volume:190 Page:
ISSN:0167-6369
Container-title:Environmental Monitoring and Assessment
language:en
Short-container-title:Environ Monit Assess

Author:

Ateeq-ur-Rauf ,Ghumman Abdul Razzaq,Ahmad Sajjad,Hashmi Hashim Nisar

Publisher

Springer Science and Business Media LLC

Subject

Management, Monitoring, Policy and Law,Pollution,General Environmental Science,General Medicine

Link

http://link.springer.com/article/10.1007/s10661-018-7012-9/fulltext.html

Reference60 articles.

1. Aalinejad, M. H., Dinpashoh, Y., & Jahanbakhsh, A. S. L. (2016). Impact of climate change on runoff from snowmelt by taking into account the uncertainty of GCM models (case study: Shahrchay Basin in Urmia). European Online Journal of Natural and Social Sciences, 5(1), 200.

2. Adnan, R. M., Yuan, X., Kisi, O., & Anam, R. (2017a). Improving accuracy of river flow forecasting using LSSVR with gravitational search algorithm. Advances in Meteorology, 3, 1–23.

3. Adnan, R. M., Yuan, X., Kisi, O., & Yuan, Y. (2017b). Streamflow forecasting using artificial neural network and support vector machine models. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), 29(1), 286–294.

4. Aichouri, I., Hani, A., Bougherira, N., Djabri, L., Chaffai, H., & Lallahem, S. (2015). River flow model using artificial neural networks. Energy Procedia, 74, 1007–1014.

5. Alfarisy, G. A. F., & Mahmudy, W. F. (2016). Rainfall forecasting in Banyuwangi using adaptive neuro fuzzy inference system. Journal of Information Technology and Computer Science, 1, 65–71.

Cited by 39 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Hydrologic interpretation of machine learning models for 10-daily streamflow simulation in climate sensitive upper Indus catchments;Theoretical and Applied Climatology;2024-04-10

2. Enhanced streamflow prediction using SWAT’s influential parameters: a comparative analysis of PCA-MLR and XGBoost models;Earth Science Informatics;2023-11-13

3. Delay-aware karst spring discharge prediction;Journal of Hydrology;2023-11

4. Machine learning method is an alternative for the hydrological model in an alpine catchment in the Tianshan region, Central Asia;Journal of Hydrology: Regional Studies;2023-10

5. A comparison of the performance of SWAT and artificial intelligence models for monthly rainfall–runoff analysis in the Peddavagu River Basin, India;AQUA — Water Infrastructure, Ecosystems and Society;2023-08-14