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The world is facing an unprecedented problem in safeguarding 0.4 % ofpotable water, which is gradually depleting day-by-day. From a literaturesurvey it has been observed that the refractive index (RI) of water changeswith a change in salinity or total dissolved solids (TDS). In this paper wehave proposed an automatic system that can be used for real-time evaluationof salinity or TDS in drinking water. A photonic crystal (PhC) based ringresonator sensor has been designed and simulated using the MEEP (MITElectromagnetic Equation Propagation) tool and the finite difference timedomain (FDTD) algorithm. The modelled and designed sensor is highly sensitiveto the changes in the RI of a water sample. This work includes areal-time-based natural sequence follower, which is a machine learningalgorithm of the naive Bayesian type, a sequence of statistical algorithmsimplemented in MATLAB with reference to training data to analyse the samplewater. Further interfacing has been done using the Raspberry Pi device toprovide an easy display to show the result of water analysis. The mainadvantage of the designed sensor with an interface is to check whether thesalinity or TDS in drinking water is less than 1000 ppm or not. If it isgreater than or equal to 2000 ppm, the display shows “High Salinity/TDSObserved”, and if ppm are less than or equal to 1000 ppm, then the displayshows “Low salinity/TDS Observed”. The proposed sensor is highly sensitiveand it can detect changes in TDS level because of the influence of anydissolved substance in water

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