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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: 8 | Views: 365

Author(s)

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

Affiliation(s)

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

ABSTRACT

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.

KEYWORDS

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

CITE THIS PAPER

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: http://dx.doi.org/10.23977/acss.2020.040105.

REFERENCES

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