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Comparison performance of artificial neural network based method in estimation of electric conductivity in wet and dry periods: Case study of Gamasiab river, Iran | ||
| Journal of Applied Research in Water and Wastewater | ||
| مقاله 2، دوره 6، شماره 2، مهر 2019، صفحه 88-94 اصل مقاله (1.19 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22126/arww.2019.1357 | ||
| نویسندگان | ||
| Nasrin Abozari1؛ Mohammadreza Hassanvand2؛ Amir Hossein Salimi* 2؛ Salim Heddam3؛ Hossein Omidvar Mohammadi4؛ Amir Noori5 | ||
| 1Department of Water Resources Engineering, Faculty of Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran. | ||
| 2Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran. | ||
| 3Department of Hydraulics Division, Faculty of Civil Engineering, Skikda University, Skikda, Algeria. | ||
| 4Department of Water Engineering and Hydraulic Structure, Faculty of Civil Engineering, Tarbiat Modares University, Tehran, Iran. | ||
| 5Department of Water Engineering and Hydraulic Structure, Faculty of Civil Engineering, Razi University, Kermanshah, Iran. | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Water quality؛ Electric conductivity؛ Artificial neural network؛ Genetic algorithm؛ Particle swarm algorithm؛ Imperialist competitive algorithm؛ Gamasiab river | ||
| مراجع | ||
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