## 5 Results and Discussions  
### Prediction of Hurricane Intensity  
This section depicts the results of predicting the hurricane intensity using the improved CNN model.  
The accuracy of the modified CNN architecture for estimating the hurricane intensity using Infrared satellite imagery for Atlantic and Pacific regions is depicted in Figure 11a-e. Recent studies use various types of CNN models such as VGG, AlexNet, ResNet, and InceptionNet depending on the dataset and the number of convolutional layers used for predicting the intensity and classifying the damages caused after disaster. Root mean square error (RMSE) and mean squared error loss (MSE) values are computed, and the error values of the k-fold validation is represented in Table 4, where the value of k is 5. Table 4 describes about five-fold validation, and the lower RMSE values are arrived at with 7.6 knots. The batch normalization normalizes each input after the first 2D convolution layer by computing the mean and variance value. Error values are higher before removing max pooling layers. After removing the max pooling layers  
Figure 12 depicts the absolute error values of the different categories of hurricanes based on the intensity estimation of the satellite images. The hurricane strength and the absolute error values are plotted. Totally, 248 samples are tested for tropical depressions, 355 samples for tropical storms, 55 samples for category 1, 19 samples for category 2, 13 samples for category 3, and three samples for category 4. By training the proposed model, the lower error values are achieved through better model initialization of the input by estimating the intensity values. The sample images at different knots using data augmentation and transfer learning are depicted in Figure 13a,b. Two new images are generated if the tropical cyclone intensity value is less than 75 knots. Six new images are generated if the intensity value is between 75 and 100 knots. Table 5 shows the performance evaluation using the optimizers Adam and RMSProp. Optimizers are used to learn the new weights and the learning rates in the network and to minimize the loss. RMSProp is the gradient-based optimization technique, which is used to balance the step size through the normalization process and avoids the vanishing gradient problem. Adam optimizers are used to handle the sparse gradients and are slower to change their direction. Twelve new images are generated if the value is greater than 100 knots. The results of the Adam optimizer are slightly higher than the RMSProp, and the RMSProp optimizer provides good results with lower RMSE values. Overall, removing max pooling layers provides better results. The addition of batch normalization and dropout layers further improves the performance. The optimizer performances with different techniques are compared in Table 5. The best performance result is obtained for the combined techniques of removing the max pooling layers and adding the batch normalization with dropout. A MAE value of 6.68 knots and an RMSE value of 7.6 knots are obtained using the proposed technique.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Validation Fold** & **Fold 1** & **Fold 2** & **Fold 3** & **Fold 4** & **Fold 5** \\ \hline RMSE & 17.8 knots & 17.4 knots & 8.2 knots & 8.1 knots & 7.6 knots \\ \hline \hline \end{tabular}
\end{table}
Table 4: RMSE values for k-fold validation.  
Figure 11: (**a**) One-fold validation; (**b**) two-fold validation; (**c**) three-fold validation; (**d**) four-fold validation; (**e**) five-fold validation.