## 4 Results and Discussion
### ResNets Prediction Analysis  
The ResNets model was trained for a relatively large number of epochs, 1500, and converged without overfitting the training data (Figure 13). Table 4 shows the performance of the model with respect to learning the pattern of data behavior where the generated results by the model were compared to the original data. This comparison shows that the model achieved high accuracy according to the performance metrics presented in Table 2. The model was able to achieve a very low MAE of \(\sim\)0.02, MSE of \(\sim\)0.005, and R\({}^{2}\) of \(\sim\)0.99. This applies to both the 504 locations scenario and the 126 locations scenario of solar irradiation arrays. There is a slight difference in R\({}^{2}\) in the range of [0.001-0.002], between the two scenarios. The first scenario required more pre-processing, as we averaged the solar irradiance data for every 4 adjacent locations, where each location is represented by \(1^{\circ}\times 1^{\circ}\). Since the results tend to be slightly higher at the second scenario, the first scenario with 504 locations was used while preparing the training dataset of the model, as it encourages less computational time. Figure 12: Effect of a major dust event, on solar irradiance over the AP on **(a)** 20 March 2012, **(b)** 4 April 2012; and on AOD for **(c)** 20 March 2012, and **(d)** 4 April 2012. It was found that randomizing the input records helped improve the ResNet model's performance in data prediction. Randomizing the input records is a practice that excludes the existence of any dependence between the input records. This does not affect the values of the records, but it targets the order in which the records are fed to the model during training. Initially, the data were arranged in a matrix where each point is an intersection of one longitude and one latitude. Splitting the matrix into two parts for training and testing without any further adjustment harmed the learning process. The model was able to learn the patterns within the locations of the training part and the near-testing part's locations only. Predicting the solar irradiance values of the testing part's locations that were far away from the training part's locations resulted in a high rate of disturbance in the preliminary experiments. Thus, the data have been assigned differently to the training and testing parts. The matrix values, the locations on the map, were assigned randomly, hence each part had locations that cover the whole map. This process positively affected the learning process and enabled the model to generalize over the considered area. Another pre-processing step that significantly impacted the model's performance was the format of the data records. We conducted preliminary experiments where we formatted the records in different lengths and window sizes in order to find the appropriate format that provides the model with the relative knowledge needed to learn the recurrent patterns. Predicting the next day's solar irradiance value based on the preceding days' solar irradiance one did not prove informative. However, a recurrent pattern over the years was noticed. Any given day of the considered year will probably have the same solar irradiance value with the same day in the previous years. This observation was utilized to format the data records fed to the model. The data records were built using the solar irradiance values of the same day of every year. Training the model on these records notably affected its performance.