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Kheradpisheh, Zohreh, Talebi, Ali, Rafati, Lida, Ghaneian, Mohammad Taghi, Ehrampoush, Mohammad Hassan. (1393). Groundwater quality assessment using artificial neural network: A case study of Bahabad plain, Yazd, Iran. , 20(1), 65-71. doi: 10.22059/jdesert.2015.54084
Zohreh Kheradpisheh; Ali Talebi; Lida Rafati; Mohammad Taghi Ghaneian; Mohammad Hassan Ehrampoush. "Groundwater quality assessment using artificial neural network: A case study of Bahabad plain, Yazd, Iran". , 20, 1, 1393, 65-71. doi: 10.22059/jdesert.2015.54084
Kheradpisheh, Zohreh, Talebi, Ali, Rafati, Lida, Ghaneian, Mohammad Taghi, Ehrampoush, Mohammad Hassan. (1393). 'Groundwater quality assessment using artificial neural network: A case study of Bahabad plain, Yazd, Iran', , 20(1), pp. 65-71. doi: 10.22059/jdesert.2015.54084
Kheradpisheh, Zohreh, Talebi, Ali, Rafati, Lida, Ghaneian, Mohammad Taghi, Ehrampoush, Mohammad Hassan. Groundwater quality assessment using artificial neural network: A case study of Bahabad plain, Yazd, Iran. , 1393; 20(1): 65-71. doi: 10.22059/jdesert.2015.54084

Groundwater quality assessment using artificial neural network: A case study of Bahabad plain, Yazd, Iran

Desert
مقاله 8، دوره 20، شماره 1، فروردین 2015، صفحه 65-71    اصل مقاله (211.18 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jdesert.2015.54084
نویسندگان
Zohreh Kheradpisheh* 1؛ Ali Talebi2؛ Lida Rafati2؛ Mohammad Taghi Ghaneian1؛ Mohammad Hassan Ehrampoush1
1Environmental Health Faculty, Shahid Sadoughi University of Medical Sciences. Yazd, Iran
2Faculty of Natural Resources, Yazd University, Yazd, Iran
چکیده
Groundwater quality management is the most important issue in many arid and semi-arid countries, including Iran.
Artificial neural network (ANN) has an extensive range of applications in water resources management. In this study,
artificial neural network was developed using MATLAB R2013 software package, and Cl, EC, SO4 and NO3 qualitative
parameters were estimated and compared with the measured values, in order to evaluate the influence of key input
parameters. The number of neurons in the hidden layer was obtained by the trial-and-error method. For this purpose, data
from 260 water samples of 13 wells in Bahabad plain were collected during 2003- 2013. The results show that the
performance of ANN model was more accurate for Cl (R=0.96), EC(R=0.98), and SO4(R=0.95), using back-propagation
algorithms according to the best chosen input parameters. It was observed that the use of ANN model for NO3 was not
very accurate, perhaps this was because of the different water sources or the impact of other parameters; thus, this result is
in contrast with the study of Diamantopoulou et al. (2005). However, this study confirms that the number of neurons in
the hidden layer cannot be found using a specific formula (double the number of inputs plus one) for all parameters but
can be obtained using a trial-and-error method.
کلیدواژه‌ها
Artificial Neural Networks؛ modeling؛ Groundwater quality؛ Water resource
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