A Review On- Water Quality Measurement System Using Artificial Intelligence

Authors(1) :-Bhagyashree Vaidya

Water is vitally important to every aspect of our lives. Monitoring the quality of the drinking water is essential as polluted water can cause deadly diseases. Usually in conventional water quality measurement systems, complexometric and colorimetric titration methods were being used, which yields results slowly. In this paper different physical and chemical water quality parameters like pH, turbidity, conductivity, total dissolved solids(TDS) and dissolved oxygen etc. are measured using different sensors. The data obtained from these sensors will be sent to the PC (LabVIEW) where this data is analyzed, and the water quality indicators are compared with the reference data provided by Indian Standards Institute (ISI) and Bureau of Indian Standards(BIS) and results are displayed as per the requirement. This paper proposes the technique to combine and infer the multi-sensor data to get the water quality result, by which accurate results can be obtained. As the water quality is subjective by nature and highly indeterminate, which causes uncertainties in the data. To overcome data uncertainties problem, this paper proposes fuzzy logic model for acquiring the accurate water quality.

Authors and Affiliations

Bhagyashree Vaidya
Department of EIE, Dayananda Sagar College of Engineering, Bengaluru, Karnataka, India

Water Quality, LabVIEW, Multi-Sensors and Fuzzy logic.

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Publication Details

Published in : Volume 4 | Issue 6 | May-June 2018
Date of Publication : 2018-05-08
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 625-627
Manuscript Number : CSEIT1846116
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Bhagyashree Vaidya, "A Review On- Water Quality Measurement System Using Artificial Intelligence", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 6, pp.625-627, May-June-2018.
Journal URL : http://ijsrcseit.com/CSEIT1846116

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