Perspectives in Water Quality Assessment
N D S S Kiran Relangi1, Aparna Chaparala2, Radhika Sajja3
1N D S S Kiran Relangi, Research Scholar Part-Time, Department of CSE, College of Sciences, Acharya Nagarjuna University, Guntur (A.P), India.
2Aparna Chaparala, Associate Professor, Department of CSE, RVR & JC College of Engineering, Guntur (A.P), India.
3Radhika Sajja, Professor, Department of Mechanical Engineering, RVR & JC College of Engineering, Guntur (A.P), India.
Manuscript received on 02 July 2019 | Revised Manuscript received on 12 August 2019 | Manuscript Published on 27 August 2019 | PP: 7-11 | Volume-8 Issue-2S4 July 2019 | Retrieval Number: B10020782S419/2019©BEIESP | DOI: 10.35940/ijrte.B1002.0782S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: One can assess the quality of water by water quality index method and it is a mathematical method to evaluate the water quality based on physical or chemical parameters, using WQI one can evaluate the water quality of both ground water and surface water, while determining the WQI one can use the standards or guidelines provided by some standard organizations like WHO (world health organization), National Standard Body of India (BIS), ICMR (Indian Council for Medical Research) etc. To assign grade to the water samples under study, apart from the WQI there are some other popular techniques used to evaluate water quality they are statistical methods, multivariate statistics, neural networks, fuzzy logic and machine learning algorithms. The aim of this study is to give insight into various methods used or developed to evaluate water quality of both ground and surface water by earlier research works.
Keywords: Cluster Analysis, Principal Component Analysis, Factor Analysis, Multivariate Statistics, Geographic Information Systems, Water Quality Index, Artificial Neural Networks, Absolute Principal Component Sources, Multi Linear Regression, Long Short Term Neural Network, Absolute Principal Component Sources-Multi Linear Regression, Multilayer Perceptron Neural Network, Radial Basis Function.
Scope of the Article: Quality Assurance Process, Standards, and Systems