سامانه پشتیبانی خدمات اطلاعات

  اخبار و اعلانات

 آمار

تعداد نشریات161
تعداد شماره‌ها6,532
تعداد مقالات70,500
تعداد مشاهده مقاله124,087,483
تعداد دریافت فایل اصل مقاله97,190,566
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
مراجع
Abu-Khalaf, N., S.Khayat, B. Natsheh, 2013. Multivariate
Data Analysis to Identify the Groundwater Pollution
Sources in Tulkarm Area/Palestine. Science and
Technology, 3; 99-104.
Badeenezhad, A., M. Gholami, A. JonidiJafari, A. Ameri,
2012. Factors affectingnitrate Concentrationsin Shiraz
Groundwater Using Geographical Information System
(GIS). Tolooe Behdasht, 11; 47- 56.
Chitsazan, C., G.H. Rahmani, A .Neyamadpou, 2013.
Groundwater level simulation using artificial neural
network: a case Study from Aghili plain, urban area of
Gotvand, south-west Iran. Geopersia Journal, 3; 35-46.
Chowdhury, SH., P. Champagne, P.J. McLellan, 2009.
Models for predicting disinfection byproduct (DBP)
formation in drinking waters: A chronological review.
Science of the Total Environment, 407; 4189–4206.
Cuesta Cordoba, I. G. A., 2011. Using OF Artificial Neural
Network for Evaluation and Prediction of Some
Drinking Water Quality Parameters within a Water
Distribution System. Water Management and Water
Structures, 3; 1-11.
Diamantopoulou, M. J., V. Z. Antonopoulos, D. M.
Papamichail, 2005. The Use of a Neural Network
Technique for the Prediction of Water Quality
Parameters of Axios River in Northern Greece.
European Water, 11; 55-62.
Dutta, S., A.A. Parsons, C. Bhattacharjee, 2010.
Development of an artificial neural network model for
adsorption and photocatalysis of reactive dye on TiO2
surface. Expert Systems with Applications, 37; 8634–
8638.
Garcia, L.A., A. Shigidi, 2006. Using neural networks for
parameter estimation in ground water. Journal of
Hydrology, 318; 215–231.
Huang, J., J. Xu, X. Liu, L. Wang, 2011. Spatial
distribution pattern analysis of groundwater nitrate
nitrogen pollution in Shandong intensive farming
regions of China using neural network method.
Mathematical and Computer Modelling, 54; 995–1004.
Karul, C., S. Soyupak, A.F.Cilesiz, 2000. Case studies on
the use of neural networks in eutrophication modeling.
Ecological Modelling, 134; 145–152.
Kheradpisheh, Z., S.A. Almodaresi, Y. Khaksar, L. Rafati,
2014. Zoning of groundwater Contaminated by Nitrate
Using Geostatistics Methods (Case Study: Bahabad
Plain, Yazd, Iran). Desert, 19; 83-90.
Kulkarni, P., SH. Chellam, 2010. Disinfection by-product
formation following chlorination of drinking water:
Artificial neural network models and changes in
speciation with treatment. Science of the Total
Environment, 408; 4202–4210.
Li, X., S. Lungcang, L. Liu, 2012. Sensitivity analysis of
groundwater level in Jinci Spring Basin (China) based
on artificial neural network modeling. Hydrogeology
Journal, 20; 727–738.
Ming Kuo, Y., Ch. Wuing Liu, K. Hung Lin, 2004.
Evaluation of the ability of an artificial neural
networkmodel to assess the variation of groundwater
quality in an area of blackfoot disease in Taiwan. Water
Research, 38; 148–158.
Moosavi, V., M. Vafakhah, B. Shirmohamadi, 2013. A
Wavelet-ANFIS Hybrid Model for Groundwater Level
Forecasting for Different Prediction Periods. Water
Resources Management, 27; 1301-1321.
Nourani, V., R. Goli Ejlali, 2012. Quantity and Quality
Modeling of Groundwater by Conjugation of ANN and
Co-Kriging Approaches. Water Resources Management
and Modeling, Available from:
http://www.intechopen.com/books/water-resourcesmanagementand-
modeling/quantity-and-qualitymodeling-
of-groundwater-by-conjugation-of-ann-andco-
krigingapproaches
Panda, S.S., V. Garg, I. Chaubey, 2004. Artificial Neural
Networks Application in Lake Water Quality
Estimation Using Satellite Imagery. Journal of
Environmental Informatics, 4; 65-74
Palani, S., S.Y. Liong, P. Tkalich, 2008. An ANN
application for water quality forecasting. Marine
Pollution Bulletin, 56; 1586–1597.
Sahoo, G.B., C. Ray, H.F. Wade, 2005. Pesticide
prediction in ground water in North Carolina domestic
wells using artificial neural networks. Ecological
Modelling, 183; 29–46.
Sadiq, R., M. J. Rodriguez, 2004. Disinfection by-products
(DBPs) in drinking water and predictive models for
their occurrence: a review. Science of the Total
Environment, 321; 21-46.

Seyam, M., Y. Mogheir, 2011. Application of Artificial
Neural Networks Model as Analytical Tool for
Groundwater Salinity. Journal of Environmental
Protection, 2; 56-71.
Sharifi, Z., A. ASafari, 2012. Assessment of Arsenic,
Nitrate and Phosphorus Pollutions in Shallow
Groundwater of the Rural Area in Kurdistan Province
(Iran). Current World Environment, 7; 233-241.
Singh, K.P., A. Basant, A. Malik, 2009. Artificial neural
network modeling of the riverwater quality-A case
study. Ecological Modelling, 220; 888–895.
Venkat Kumar, N., S. Mathew, G. Swaminathan, 2010.
Analysis of Groundwater for Potability from
Tiruchirappalli City Using Backpropagation ANN
Model and GIS. Journal of Environmental Protection, 1;
136-142.
WRS, 2014. Available From http://wrs.wrm.ir/m3/istgahbaranlist.
asp?recperpage=ALL. Accessed 1st May 2014
Ying, Z., N. Jun, C. Fuyi, 2007. Water quality forecast
through application of BP neural network at Yuqiao
reservoir. Journal of Zhejiang University Science, 8;
1482-1487.

آمار
تعداد مشاهده مقاله: 2,903
تعداد دریافت فایل اصل مقاله: 3,449


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب