Document Type : Research Paper
Authors
1
Master in Civil Engineering, Department of Hydraulic Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran..
2
Assistant Professor, Department of Hydraulic Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
3
Assistant Professor, Department of Hydraulic Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
Abstract
Investigating changes in the quality of surface water has always been one of the most important issues for drinking, agriculture and industry, In this research, the water quality of the Karun River which is one of the largest sources of surface water supply in different parts of Khuzestan province, was studied at the Gatund water measuring station located downstream of the Gatund dam and Ahvaz water measuring station. The results indicate that during the years 2008 to 2011 and 2013 to 2015, which experienced lower than average precipitation, reflecting drought conditions, the reduction in river discharge has led to an increase in TDS (Total Dissolved Solids) and EC (Electrical Conductivity) values. According to the Piper diagram, the overall status of water samples taken at the Gotvand station shows a tendency towards non-carbonate hardness after the operation of the Gotvand dam in 2011. The water samples at this station exhibit anionic characteristics in the chloride zone or a mixed zone, and in terms of cations, they are positioned in the sodium-potassium or mixed zone. The Schoeller diagram results demonstrate that the water quality of the Karun River in terms of drinking water standards is acceptable at this station
Also, the difference of water quality parameters of Karun River in dry (April to September) and wet (October to March) periods is 20% in Gatund station and 10% in Ahvaz station. The results obtained from the regression analysis of the TDS parameter in the Ahvaz hydrometric station show that machine learning models can be used as a powerful tool in predicting surface water quality , so that the performance of the decision tree model with the coefficient The determination of R2=0.97 and the multivariate regression model with the coefficient of determination R2=0.95 was used in the prediction of this parameter.
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