Nasrin Abozari; Mohammadreza Hassanvand; Amir Hossein Salimi; Salim Heddam; Hossein Omidvar Mohammadi; Amir Noori
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 ...
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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 in wet and dry periods are research priority. In this study, an optimized artificial neural network (ANN) trained with three different optimization algorithms namely; particle swarm optimization (PSO), genetic algorithm (GA) and imperialist competitive algorithm (ICA) was proposed for predicting the electric conductivity (EC). For this purpose, water quality data from 1967 to 2017 collected at the hydrometric station in the Gamasiab river were used for developing and testing the models. First, the study program was divided into two periods of wet and dry, this classification based on flow rate in the river. Then, in a preliminary statistical analysis, the effective parameters were determined for EC estimation. The performance of the applied methods showed that the ANN optimized using ICA algorithm was better than the ANN optimized with GA and PSO, and also the standard ANN without optimization. Overall, the ANN optimized with ICA has higher R and lower MARE and RMSE, with values of 11.56, 19.63 and 0.93, during the dry period, and 10.63, 17.19 and 0.97 during the wet period, respectively.
Amir Hossein Salimi; Sayed Farhad Mousavi; Saeed Farzin
Abstract
Rivers serve as one of the main sources of water supply. Human activities, salts in the soil and rocks and urban runoffs, as well as air contaminants, lead to contamination of river water. In this research, Gamasiab river, which is the upstream of Karkheh river, was selected as a case study. Sixteen ...
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Rivers serve as one of the main sources of water supply. Human activities, salts in the soil and rocks and urban runoffs, as well as air contaminants, lead to contamination of river water. In this research, Gamasiab river, which is the upstream of Karkheh river, was selected as a case study. Sixteen stations were selected along this river to determine the sulfate content of water samples. Samples were taken from these stations according to the guidelines (ISO 5667-5, 1991). The samples were then transferred to laboratory and were filtered using nanoparticles of natural clinoptilolite. The X-Ray Diffraction (XRD), Transmission Electron Microscopy (TEM) andFourier-Transform Infrared Spectroscopy (FTIR) images were taken to determine the properties of the adsorbents. The images indicated that the selected methods for preparation of the nanoparticles were correctly implemented. After examining the filtered samples, the adsorption efficiency was 95% for clinoptilolite. Whatman filter paper 42 was used for desorption of the natural nano-clinoptilolite. Adsorption isotherm of the natural clinoptilolite was Freundlich with a determination coefficient of R2=0.918. By using Design Expert software and assumption of two pH factors and adsorbent to contaminant ratios (D/C), optimum adsorption points were found and theoretical adsorption values were calculated as well. Results showed that the optimum adsorption points for clinoptilolite were pH = 9.51 (mg)adsorbent/(mg/l)initial and D/C=18.91(mg)adsorbent/(mg/l) initial. Comparison of the adsorbent function indicated that clinoptilolite had good performance in removal of sulfate ion from river water samples.
