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Classification of Groundwater Quality using Artificial Neural Networks in Safwan and Al-Zubayr in Basra

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DOI: 10.23977/acss.2020.040105 | Downloads: 16 | Views: 906


Ahmed Naser Ismael 1, Hamid Ali Abed 2, Majida Ali Abed 3


1 Management Information Systems Department, Basra University, Al-Basrah, Iraq
2 Computer Science Department, Basra University, Al-Basrah, Iraq
3 College of Computers Sciences, Tikrit University, Tikrit, Iraq

Corresponding Author

Hamid Ali Abed


Groundwater in the southern region of Iraq is a completed resource for irrigation and industry. In this study, the groundwater quality of more than 60 wells in the Safwan area and Al-Zubayr district in Basra was calculated using artificial neural networks with feedback. The variables used in the proposed classification and verification model are the physical and chemical variables of groundwater. The taxonomy model demonstrated good performance and accurate taxonomic capacity to facilitate the development and implementation of more effective and sustainable groundwater management strategies in the study area.


Groundwater Quality, Artificial Neural Networks, Safwan and Al-Zubayr, Basra


Ahmed Naser Ismael, Hamid Ali Abed and Majida Ali Abed. Classification of Groundwater Quality using Artificial Neural Networks in Safwan and Al-Zubayr in Basra. Advances in Computer, Signals and Systems (2020) 4: 25-35. DOI:


[1] Abdalaali, H.H. (2011). "The effect of soil surface mulches, irrigation and nitrogen levels on some soil      properties, tomato growth and yield". M.Sc thesis / college of agriculture / university of Basrah / Iraq. 
[2] Al  meini,  A.  K.  A.  (2010).  "proposed  index  of  water  quality  assessment  for  irrigation".  Eng.  &     Tech. Journal, 28 (22):6557-6571.
[3] Al-Abadi, M.A., 2002 : "Optimum management of model of groundwater resources in Safwan-Zubair      area, South of Iraq". Unpub. M.Sc. Thesis, College of Science, Univ. of  Basra, 110P.
[4] Al-Aboodi, A.H., 2003 : "A study on Groundwater characteristics in Safwan-Zubair area," Unpub.      M.Sc.     Thesis, College of Engineering, Uninv. of Basra, 150p. 
[5]  Atia, A.M., 2000 :" Hydrogeology of Safwan-Zubair area, South of Iraq", Unpub. M.Sc. Thesis,  College   of Science, Univ. of Basra, 90P. (In Arabic). 
[6] Dagar,  I.  C.   (2009)."Opportunities  for  alternate  land  uses  in  salty  and  water  scarcity  areas." International Journal of Ecology and Environmental Sciences, 35(1):53-66. 
[7] Efe MO (2008), “ Novel neuronal activation functions for feed forward neural networks,” Neural      Process Lett , Vol. 28, pp. 63–79..
[8] Mohammed, M. H. , Mizhir, A. A. and Al-Baʼ aj, A. K. (2010)."Determination of nitrate, nitrite and      chloride  in  ground  water  of  some  wells/  Basrah,  south  of  Iraq."  Scientific  Basrah    Research,     36(1):59-64.  
[9] Majida A. A., Hamid A. A.(2015)"High Accuracy Arabic Handwritten Characters Recognition Using      Error Back Propagation Artificial Neural Networks",(IJACSA) International Journal of Advanced      Computer Science and Applications,Vol. 6, No. 2,    
[10]  Semra.,Recep (2018) " Modelling Nitrate Prediction of Groundwater and Surface Water Using Artificial Neural Networks",Vol 2,No. 21:312-325.

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