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

Data sets

Densities and Refractive Indices of Potassium Salt Solutions in Binary Mixture of Different Compositions S. D. Deosarkar, V. V. Pandhare, and P. S. Kattekar http://dx.doi.org/10.1155/2013/368576

Design of optical sensor for detection of brininess of water J. Lavanya, S. K. Roy, and P. Sharan http://dx.doi.org/10.1109/GHTC-SAS.2014.6967566

Mapping of aqua constituents using photonic crystal P. Sharan, P. Deshmukh, and S. K. Roy http://dx.doi.org/10.1109/R10-HTC.2013.6669063

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.