Application of machine learning for real-time evaluation of salinity (or
TDS) in drinking water using photonic sensors

Roy, Sandip Kumar; Sharan, Preeta

doi:https://doi.org/10.5194/dwes-9-37-2016

Articles | Volume 9, issue 2

https://doi.org/10.5194/dwes-9-37-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/dwes-9-37-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 9, issue 2

Research article

|

26 Sep 2016

Research article |

| 26 Sep 2016

Application of machine learning for real-time evaluation of salinity (or TDS) in drinking water using photonic sensors

Sandip Kumar Roy and Preeta Sharan

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Final revised paper (published on 26 Sep 2016)
Preprint (discussion started on 13 Jun 2016)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Application of Machine Learning For Real-time Evaluation of Salinity (or TDS) in Drinking Water Using Photonic Sensor', marzieh mokarram, 27 Jun 2016
- AC1: 'Response to the feedback on the paper', SANDIP KUMAR ROY, 23 Jul 2016
RC2: 'comments and suggestions 2016-4', Anonymous Referee #2, 28 Aug 2016
- AC2: 'Thanks for your comments and suggestion', SANDIP KUMAR ROY, 04 Sep 2016

Short summary

About 20 % of the world population lives in areas without sufficient portable water; 97 % of the water on the earth is seawater. Understanding the level of salinity/TDS of seawater is a necessity. Currently, determination of salt content is by chemical analysis and is time-consuming. Our research is to develop a lab on chip optical sensor to measure the percentage of salinity in water. Even a small % of salinity change in water can be detected in real time by the sensor continuously with accuracy.