Articles | Volume 9, issue 2
https://doi.org/10.5194/dwes-9-37-2016
https://doi.org/10.5194/dwes-9-37-2016
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

Viewed

Total article views: 2,862 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,535 1,059 268 2,862 125 147
  • HTML: 1,535
  • PDF: 1,059
  • XML: 268
  • Total: 2,862
  • BibTeX: 125
  • EndNote: 147
Views and downloads (calculated since 13 Jun 2016)
Cumulative views and downloads (calculated since 13 Jun 2016)

Cited

Latest update: 30 Aug 2025
Download
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.
Share