Comparison performance of artificial neural network based method in estimation of electric conductivity in wet and dry periods: Case study of Gamasiab river, Iran

Nasrin Abozari; Mohammadreza Hassanvand; Amir Hossein Salimi; Salim Heddam; Hossein Omidvar Mohammadi; Amir Noori

Volume 6, Issue 2 , July 2019, , Pages 88-94

https://doi.org/10.22126/arww.2019.1357

Abstract
  The frequent occurrences of wet and dry in the catchment area of the Gamasiab river located in the west of Iran, in addition to affecting the quantitative status of surface water, has caused changes in the water quality of the basin. Therefore, modeling and prediction of Gamasiab river water quality ...  Read More

Estimation of barley yield under irrigation with wastewater using RBF and GFF models of artificial neural network

Yahya Choopan; Somayeh Emami

Volume 6, Issue 1 , January 2019, , Pages 73-79

https://doi.org/10.22126/arww.2019.1137

Abstract
  In this study, barley yield has been estimated via radial basis function network (RBF) and feed-forward neural networks (GFF) models of artificial neural network (ANNs) in Torbat-Heydarieh of Iran. For this purpose, a dataset consists of 200 data at three levels of irrigation with well water, industrial ...  Read More

Application of artificial neural networks for the prediction of Gaza wastewater treatment plant performance-Gaza strip

Mazen Hamada; Hossam Adel Zaqoot; Ahmed Abu Jreiban

Volume 5, Issue 1 , March 2018, , Pages 399-406

https://doi.org/10.22126/arww.2018.874

Abstract
  This paper is concerned with the use of artificial neural network and multiple linear regression (MLR) models for the prediction of three major water quality parameters in the Gaza wastewater treatment plant. The data sets used in this study consist of nine years and collected from Gaza wastewater treatment ...  Read More