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Reseach Article
A Review on Water Pollution Detection Techniques using Artificial Neural Network Methods
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 17 |
| Year of Publication: 2024 |
| Authors: Saima Khan, Md. Abidur Rahman Khan |
10.5120/ijca2024923554
|
Saima Khan, Md. Abidur Rahman Khan . A Review on Water Pollution Detection Techniques using Artificial Neural Network Methods. International Journal of Computer Applications. 186, 17 ( Apr 2024), 7-14. DOI=10.5120/ijca2024923554
Abstract
Water quality is defined by its physical, chemical and biological parameters which are interrelated. In recent years, Artificial Neural Networks (ANN) have found some applications in the area of water quality modeling. Among the various familiar methods of water pollution detection, ANN based methods are one of the most effective methods which provides satisfactory outcome to the users than other methods. This paper elucidates several techniques for detecting water pollution, as applied in diverse regional water sources, and delineates their respective findings using appropriate formats such as tables and figures. These methods for water pollution detection, along with their corresponding data, figures, tables, and results, are comprehensively presented herein alongside the relevant citations and references. This study will help to understand diverse effective techniques for evaluating water quality and detecting water pollution level. Furthermore, this study will guide for refining and developing improved methodologies within this domain.
References
- Bahaa Mohamed Khalil, Ayman Georges Awadallah, Hussein Karaman, Ashraf El-Sayed, ”Application of Artificial Neural Networks for the Prediction of Water Quality Variables in the Nile Delta”. Journal of water resource and protection 2012 (2012).
- Meini Yang , Dingfang Li , Jinbo Yang & Wei Xiong, ”FANNbased surface water quality evaluationmodel and its application in the Shaoguan area”. Geo-spatial Information Science 10, no. 4 (2007): 303-310.
- Yi-Ming Kuo, Chen-Wuing Liu, Kao-Hung Lin, ”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, no. 1 (2004): 148-158.
- Miao Qun and Li Yue , Hai Yang, He Hui and Zhang Xiaomei, s”Back Propagation Neural Network in theWater Quality Evaluation of Qingdao Dagu River”. In 2008 2nd International Conference on Bioinformatics and Biomedical Engineering, pp. 3161-3164. IEEE, 2008.
- Zhenxiang Xing , Qiang Fu, Haiyan Cui, ”Application of RAGABP ANN based on AM-MCMC in Water Quality Evaluation”. In 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1- 4. IEEE, 2008.
- Changjun Zhu, Xiujuan Zhao, Jihong Zhou, ”ANN based on PSO for Surface Water Quality Evaluation Model and Its Application”. In 2009 Chinese Control and Decision Conference, pp. 3264-3268. IEEE, 2009.
- Yi-mei Tian Li-hua Jia and Bin-wei Wang, ”Application of artificial neural network in urban landscape water quality evaluation”. In 2010 4th International Conference on Bioinformatics and Biomedical Engineering, pp. 1-4. IEEE, 2010.
- Zhaoxian Zheng , Yuling Zhang, ”Application of RBF-ANN Model in Groundwater Quality Evaluation of Changchun Region”. International Conference on Multimedia Technology, pp. 1471-1474. IEEE, 2011.
- Liu Jianlin, Zhang Guozhen,Wu Fuping, Zhang Hongwei, Yang Hao,”Study of Rainwater Quality Assessment Model Based on Radial Basis Function Artificial Neural Network”. International Symposium on Geomatics for Integrated Water Resource Management (pp. 1-4), IEEE, 2012.
- T¨ulay Ekemen Keskin, Muharrem D¨u˘genci, Fikret Kac¸aro˘glu, ”Prediction of water pollution sources using artificial neural networks in the study areas of Sivas, Karab¨uk and Bartın (Turkey)”, Environmental Earth Sciences 73 (2015), pp.5333- 5347.
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