![]() |
Application of artificial neural networks to predict total dissolved solids in the river Zayanderud, Iran
Asadollahfardi Gholamreza, Meshkat-Dini Afshin, Homayoun Aria Shiva, Roohani Nasrin
Environmental Engineering Research. 2016;21(4):333-340. Published online 2016 Jun 16 DOI: https://doi.org/10.4491/eer.2015.096
|
Citations to this article as recorded by
A survey on river water quality modelling using artificial intelligence models: 2000–2020
Tiyasha, Tran Minh Tung, Zaher Mundher Yaseen
Journal of Hydrology.2020; 585: 124670. CrossRef A Review of the Artificial Neural Network Models for Water Quality Prediction
Yingyi Chen, Lihua Song, Yeqi Liu, Ling Yang, Daoliang Li
Applied Sciences.2020; 10(17): 5776. CrossRef Eutrophication modelling of Amirkabir Reservoir (Iran) using an artificial neural network approach
Shiva Homayoun Aria, Gholamreza Asadollahfardi, Nima Heidarzadeh
Lakes & Reservoirs: Research & Management.2019; 24(1): 48. CrossRef Soft computing techniques in prediction Cr(VI) removal efficiency of polymer inclusion membranes
Muhammad Yaqub, Beytullah EREN, Volkan Eyupoglu
Environmental Engineering Research.2019; 25(3): 418. CrossRef Comparison of Box-Jenkins time series and ANN in predicting total dissolved solid at the Zāyandé-Rūd River, Iran
G. Asadollahfardi, H. Zangooi, M. Asadi, M. Tayebi Jebeli, M. Meshkat-Dini, N. Roohani
Journal of Water Supply: Research and Technology-A.2018;[Epub] CrossRef
|