  Optical Communication in Free Space (FSO) bids more radio bandwidth, operates
under a gratis license, and has a lower startup cost as compared to Radio
Frequency (RF). Nonetheless, its vulnerability to variations in atmospheric
meteorological circumstances is a concern. Ultimately, the purpose of this
study is to use Principal Component Analysis (PCA) with Artificial Neural
Networks (ANN) to design a QoS prediction model for a terrestrial FSO
communication connection. To accomplish the specified goal, meteorological data
such as visibility, wind speed, and altitude were collected from the Weather
Services in South Africa (SAWS) archive during a ten-year duration at five
different locations: George, Johannesburg, Kimberly, Bloemfontein, and
Polokwane. The eigenvalues of the first Principal Component (PC1) and the
second Principal Component (PC2) in the PCA across the stations Bloemfontein,
Johannesburg, Kimberly, George, and Polokwane are 7.624 and 1.020, 7.234, and
0.984, 6.204 and 1.723, 7.354 and 0.876, and 7.104 and 0.865, respectively,
demonstrating that, they are kept as QoS variables to train the Artificial
Neural Network (ANN) model as they provide the most compelling interpretation
of the original variable data. The RMSE values of every proposed model across
all the study locations are 0.1437, 0.2131, 0.2329, 0.1101, and 0.1977,
respectively. Based on the RMSE, the proposed performed better over George. A
realistic and accurate predictive model is developed for each of the study
locations. Thus, the developed model will serve as a valuable tool for
maintaining good QoS in FSO network services and improving telecom businesses
in South Africa.
