**4. Results**

This section presents the results for the data-processing steps introduced previously. To visually aid in the comprehension of the plots, Figure 7 shows how to interpret the *x*-axis of figures using the aforementioned index.

**Figure 7.** Visual guide to aid in the interpretation of the *x*-axis of the analysis plots with multiple networks.

Starting with the larger ticks, with the showing indexes, these mark the start of a new Δ*t* value. As for the smaller ticks those correspond to the different ROI radii used within each group of Δ*t* groupings.

#### *4.1. Correlation Analysis*

The first set of correlation analysis results, between *P*<sup>0</sup> and the evaluated variables, is presented in Figure 8.

**Figure 8.** Correlation between *P*<sup>0</sup> and the evaluated variables.

At shorter time intervals, there is a high correlation between *P*<sup>0</sup> and *P*−1; however, as Δ*t* increases, the correlation coefficient approaches zero. Temperature and image attributes present correlation coefficient magnitudes under 0.4, which means that a linear regression model is unsuitable to represent these data. Next, the correlation coefficients between Δ*P* and the same 5 variables in the previous analysis is presented in Figure 9.

**Figure 9.** Correlation between Δ*P* and the evaluated variables.

The results for Δ*P* present rather different relationships between the chosen variables. As Δ*t* increases the correlation coefficient magnitudes mostly increase as opposed to the results with *P*0. In the case of the image attributes, it reaches a value of about 0.7 at 75 s before drastically falling and becoming negative. *P*−<sup>1</sup> does correlate better to Δ*P* than to *P*0; however, it is still too low for a proper linear regression model. What these results point out is that a linear model is unsuitable for representing this phenomenon through

these data. The next step is to use a nonlinear model to attempt this representation, and the chosen model for that was a MLP (multilayer perceptron) artificial neural network.
