## 4 Conclusions  
In this study, a machine learning based scheme was introduced to estimate the RSSI parameter of an FSO link over a maritime environment based on macroscopic meteorological measurements. To test the proposed approach, a large experimentally derived data set was used, which included seven parameters (i.e., air temperature, wind speed, solar flux, dew point, relative humidity, air pressure and air-sea temperature difference) obtained over a twelve-month period from a commercial FSO system and a weather station, respectively. Five popular ML algorithms were trained in order to construct a robust model to accurately predict the link's performance in terms of received signal strength. The results showed a significant improvement compared to traditional regression modeling techniques (i.e. polynomials) and their prediction accuracy performance measured by the coefficient of correlation R\({}^{2}\) and the RMSE was extremely promising for even more complex predictive modeling. The significance of each of the independent variables was also studied with the gradient boosting regression algorithm by using two different approaches. Both of them showed that air temperature influences the output parameter the most. While all five ML methods showed a high degree of RSSI prediction accuracy, the ANN approach resulted in the most accurate model in terms of R\({}^{2}\) (i.e., 0.94867) while the RF in terms of RMSE values (i.e., 7.37). ANN and GBR did require a significant computational time, while the three other methods gave their results in a much shorter training time. Overall, this study provided a thorough insight to RSSI prediction accuracy using different machine learning methods that proved to be notably accurate to model such a relationship of complex mechanisms. The superlative performance of the ML approaches comparing to the common-used regression method indicates that, first, ML is the appropriate modelling choice when overall prediction is the goal and the volume of data is high; second, allows the ability to tune hyper parameters per ML approach to enable optimal performance; and, finally, the trained models can be either improved by adding more data or be used for continuous streamflow RSSI predictions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approach** & \multicolumn{2}{c}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**RMSE**} \\ \hline  & **Training** & **Validation** & **Test** & **Training** & **Validation** & **Test** \\ \hline Baseline & 0.36 & - & 0.05 & - & - & - \\ KNN & 0.93 & - & 0.85 & 8.29 & - & 12.48 \\ DT & 0.9764 & - & 0.91 & 4.9 & - & 9.71 \\ RF & 0.994 & - & 0.947 & 2.7 & - & 7.37 \\ GBR & - & - & 0.9417 & - & - & 7.71 \\ ANN & 0.9496 & 0.9468 & 0.94867 & 10.06 & 10.19 & 10.17 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of the baseline and five ML algorithm models for RSSI prediction in terms of R-squared (R\({}^{2}\)) and root mean square error (RMSE).  
A.L.: Conceptualization, Methodology, Software, Validation, Data Curation, Writing Draft K.P.: Software, Formal Analysis, Review, Editing, Supervision, Project Administration H.E.N.: Formal Analysis, Review, Editing, Supervision, Project Administration A.T.: Formal Analysis, Review, Editing, Resources, Supervision, Project Administration K.C.: Software, Formal Analysis, Review, Editing, Supervision A.Z.: Methodology, Formal Analysis, Review. All authors have read and agreed to the published version of the manuscript.  
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.  
Not applicable.  
The authors declare no conflict of interest.