# Aquasafe: A Remote Sensing, Web-Based Platform for the Support of Precision Fish Farming
## 3 Results
### Real-Life Cases
#### 3.3.2 Dissolved Oxygen Model Performance  
To evaluate the performance of the dissolved oxygen model, we used 20% of the dataset and calculated two metrics: R-squared (R\({}^{2}\)) and mean absolute error (_MAE_). R\({}^{2}\) is used to examine the fitness of the model, and it expresses how well the regression line approximates the real data point without considering all parameters. On the other hand, the _MAE_ expresses the average distance between the estimated and the real values, which is useful to calculate the precision (Equation (1)):  
\[MAE=\sum_{i=1}^{n}\frac{|y_{i}-x_{i}|}{n} \tag{1}\]  
where \(y_{i}\) is the predicted values and \(x_{i}\) the measured values.  
Figure 13: Oxygen consumption rate for gilthead seabream during the period 1 April 2022–1 October 2022. The lines represent the Aquasafe platform’s outputs at the three study areas.  
_MAE_ was found to be 0.33, indicating that the average distance between the predicted and real values is 0.33 mg/L (Table 2). This value is considered acceptable for most real-life applications for the estimation of dissolved oxygen. The residuals were well balanced around zero, indicating the absence of any systematic error. The measured values ranged between 4.5 and 8 mg/L, and the predicted values ranged between 5.5 and 7.5 mg/L (Figure 15).  
R2 value was calculated as 0.67 (Table 2), indicating its suitability for real-world applications. Furthermore, the time-series plot indicated distinct seasonality in temperature and dissolved oxygen levels (Figure 16). Specifically, temperatures were higher during summer months (June to September), while dissolved oxygen levels were lower in the same period. The correlation between temperature and dissolved oxygen levels was negative, with elevated temperature values coinciding with reduced dissolved oxygen levels and vice versa (Figure 17). Notably, a comparison of predicted and measured values demonstrated a close match, with the model accurately capturing the underlying patterns and trends in both parameters.  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **Regression Statistics** \\ \hline R-Squared & 0.67 \\ Mean Absolute Error & 0.33 \\ Observations & 136 \\ \hline \hline \end{tabular}
\end{table}
Table 2: To evaluate the performance of the dissolved oxygen model, we used 20% of the dataset, which corresponds to 136 observations, and calculated two metrics: R-Squared (R2) and mean absolute error (MAE).  
Figure 14: Biomass progression for gilthead seabream during the period 1 April 2022–1 October 2022 under low (<10 kg m\({}^{3}\)) and high (>30 kg m\({}^{3}\)) stocking density. The lines represent the Aquasafe platform’s outputs at the three study areas, while the points denote alerts generated by the system.  
Figure 16: Time-series plot of sea surface temperature and dissolved oxygen predictions and measured values showing distinct seasonality.  
Figure 17: The correlation between the measured and predicted values of dissolved oxygen (DO), along with the measured and satellite values of sea surface temperature (SST), is illustrated in the heatmap.  
Figure 15: The scatter plot depicts the predicted versus the measured dissolved oxygen levels, showing a close match between them, with R\({}^{2}\) equal to 0.67.