## 5 Results and Discussions
### Hurricane Damage Prediction  
This section presents the results and analysis of hurricane damage prediction after Hurricane Harvey in 2017. The satellite imagery data are divided into training, testing, and validation. In Figure 14, training, testing, and validation split, and the labels with the damage and no damage classes with respect to latitude and longitude are plotted. The validation split is considered from the training split data. Figure 15 shows the distribution of RGB values. In the histogram, the x-axis is split into number ranges or the bins where the width of the bar represents the range of the bin and the height represents the total number of data points that lies within that range. The y-axis represents the frequency or count of the distribution of components in a particular bin. Figure 16 shows the sample images of damage/no damage classes. Data transformation takes place where the data are converted to float32 and scaling is done from an unsigned integer in the range of 0 to 255 to float32 with normalized values between 0 to 1. Image augmentation is applied by rotating through 90, 180, or 270 degrees with random flips. The augmented and the original datasets are merged together. The number of samples in the training and validation sets are doubled using image augmentation. Figure 17 shows the results after data preprocessing, where the original dataset is transformed to the RGB format. Initially, the batch size is fixed as 32, and the base model is created from the pre-trained model VGG 19. It converts 128 \(\times\) 128 \(\times\) 3 image to a 4 \(\times\) 4 \(\times\) 512 set of feature blocks. Tables 6 and 7 represent the configuration of VGG 19 layers, and the total number of trainable and non-trainable parameters are found after average pooling and dense layers. Table 8 shows the number of trainable and non-trainable parameters after fine-tuning the VGG 19 layers. Transfer learning can be achieved through feature extraction or fine-tuning of layers. Fine-tuning of the layers outperforms the feature extractor technique and provides higher accuracy. Initially, by training the network with the small learning rate, and through the previously learned convolutional layers, a new set of fully connected layers can learn the patterns. Applying fine-tuning leads to higher accuracy, the last few layers of VGG 19 from the layer 15 to  
Figure 12: Absolute errors of different hurricane categories. layer 19 are fine-tuned for damage prediction, and automatic annotation of the images is done using the model. The baseline model achieves about 80% accuracy before fine-tuning the layers and is represented in Figure 18. After fine-tuning, the model with 98% accuracy is achieved using VGG 19 and is represented in Figure 19. The critical success index (CSI) is 0.80 and the false alarm rate (FAR) for hurricane damage detection is 0.91. Figure 13: _Cont._  
Figure 13: (**a**) Sample satellite images after data augmentation. (**b**) Generation of satellite images after data augmentation. Figure 14: Training and validation test split. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Optimizer/Technique Used** & \
\begin{tabular}{c} **Mean Absolute Error (MAE)** \\ **Knots** \\ \end{tabular} &
\begin{tabular}{c} **Root Mean Square Error** \\ **(RMSE) Knots** \\ \end{tabular} & **Relative RMSE** \\ \hline
**RMSProp** (no max-pooling, no batch normalization, and only dropout) & 7.21 kts & 9.47 kts & 0.19 \\ \hline
**RMSProp** (no max-pooling, with batch normalization and dropout) & **6.68 kts** & **7.6 kts** & **0.17** \\ \hline
**Adam** optimizer (no max-pooling, no batch normalization, and only dropout) & 8.68 kts & 10.18 kts & 0.22 \\ \hline
**Adam** optimizer (no max-pooling, with batch normalization and dropout) & 8.52 kts & 10.04 kts & 0.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance evaluation using different optimizers. Figure 15: Histogram distribution of RGB components. Figure 16: Sample damage/no damage classes read in BGR. Figure 17: Information flow through filters of CNN layers.