## 4 Results and Discussion  
Figures 5-8 show that all the factors used are generating a specific \"fingerprint\" or change in data input parameters, which is then used to generate a machine learning model based on those points as a variable input parameter. The \"fingerprint\" term here means that a specific pattern of input-output data is unique, as demonstrated previously by changes in the distance \(d\) and its dependence on other input parameters. This assures that the MLP model can fit input to output because no input set is unique. Tables 4 and 5 show the MLP training/testing errors and accuracy parameters.  
In Table 4, the error outputs parameters show that the MLP model accuracy is very high with \(R^{2}=0.844\) and the mean absolute error is 0.156. This demonstrates that the proposed MLP model can be used for input-output mapping purposes. To make the data more interesting, Table 4 shows a tabular form of the data with new input parameters and MLP predictions, along with the corresponding error or classification. It is important to mention that the input data in Table 4 are data not used during the training stage (\(W_{water}=8\) cm, \(T=40\)\({}^{\circ}\)C). This table displays the predictions of 90 datasets per sample, each with a final classification. It is obvious that with more correct predictions, water's  
\begin{table}
\begin{tabular}{l c} \hline Coefficient of Determination R\({}^{2}\) & 0.844 \\ Mean Squared Error MSE & 0.155 \\ Mean Abs Real Error MARE & 0.326 \\ Mean Square Log Error MSLE & 0.075 \\ Root Mean Squared Error RMSE & 0.395 \\ Mean Absolute Error MAE & 0.156 \\ Root Mean Squared Logarithmic Error RMSLE & 0.274 \\ \hline \end{tabular}
\end{table}
Table 4: MLP model classification errors.  
Figure 8: Effect of wavelength on the distance \(d\) of pure water sample for different angle of incidence \(\theta_{i}\) at \(T=30\)\({}^{\circ}\)C and \(W_{water}=5\) cm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number of Total**} & \multirow{2}{*}{**Data Set Size Angle**} & \multicolumn{2}{c}{**MLP-Multi-Layer Perceptron Classifier**} \\ \cline{3-4}  & & **Correct** & **Wrong** \\  & & **Classification Count** & **Classification Count** \\ \hline
90 & Pure water (\(S=0\)) & 73 & 17 \\
90 & Salt water (\(S=35\%\)) & 66 & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 5: MLP model classification test on new inputs (pure water).  
final classification will be guaranteed. It is also worth noting that a similar prediction for salty water is observed.  
The results presented in Table 5 show the model prediction on the unfamiliar data input which is the key point that describes the model accuracy. In this table, the classification is based on the incident angle \(\theta_{i}\) (\({}^{\circ}\)) range, considering 90 samples of data. The results show that there are more correct prediction samples than incorrect prediction samples, resulting in a correct overall water sample classification. In combination with Table 4, the overall model results are adaptable. The error parameters shown in Table 4 are based on the training stage with 3054 inputs, from which 70% are used in the training stage and 30% are used to test and calculate the given errors plus an additional 10 inputs used for MLP model calibrations.  
This research is a proof of concept of the proposed sensor design. It consists of a new method of water classification combining optics concepts with the machine learning model. Different simulations using COMSOL have been performed, but no prototype has been developed at this time. The obtained COMSOL simulation results have been validated by comparing them to values obtained from experimental work and to analytical equations. Results showed a good agreement.