## 5 Results and Discussions
### Hurricane Risk Mitigation  
It is important to understand the risk imposed due to the occurrence of hurricanes and to take necessary steps to avoid significant losses. Human lives can be saved and economic losses can be reduced by implementing the policies and measures in advance and by imparting awareness to the public. The hurricane risk in the Caribbean and Central America is high. Due to the technological advancement in prediction models, the tropical depression can be predicted accurately, and an early warning can be given to the public. Countries can include the mitigation measures by considering the details such as distribution of the occurrence of hurricanes, wind speed and direction, height of the storm surges, etc. The mitigation measures adopted should consider the long-term effects. Additionally, hurricanes cause serious health hazards to people and lead to various types of diseases. The disease incurred might last for months or even years. Building codes can be used to control the building designs, methods, and materials. Formulating the mitigation strategy is an essential step in vulnerability analysis and risk assessment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer (Type)** & **Output Shape** & **Param \#** \\ \hline input\_1 (Input Layer) & (None, 224, 224, 3) & 0 \\ block1\_conv1 (Conv2D) & (None, 224, 224, 64) & 1792 \\ block1\_conv2 (Conv2D) & (None, 224, 224, 64) & 36,928 \\ block1\_pool (MaxPooling2D) & (None, 112, 112, 64) & 0 \\ block2\_conv1 (Conv2D) & (None, 112, 112, 128) & 73,856 \\ block2\_conv2 (Conv2D) & (None, 112, 112, 128) & 147,584 \\ block2\_pool (MaxPooling2D) & (None, 56, 56, 128) & 0 \\ block3\_conv1 (Conv2D) & (None, 56, 56, 256) & 295,168 \\ block3\_conv2 (Conv2D) & (None, 56, 56, 256) & 590,080 \\ block3\_conv3 (Conv2D) & (None, 56, 56, 256) & 590,080 \\ block3\_conv4 (Conv2D) & (None, 56, 56, 256) & 590,080 \\ block3\_pool (MaxPooling2D) & (None, 28, 28, 256) & 0 \\ block4\_conv1 (Conv2D) & (None, 28, 28, 512) & 1,180,160 \\ block4\_conv2 (Conv2D) & (None, 28, 28, 512) & 2,359,808 \\ block4\_conv3 (Conv2D) & (None, 28, 28, 512) & 2,359,808 \\ block4\_conv4 (Conv2D) & (None, 28, 28, 512) & 2,359,808 \\ block4\_pool (MaxPooling2D) & (None, 14, 14, 512) & 0 \\ block5\_conv1 (Conv2D) & (None, 14, 14, 512) & 2,359,808 \\ block5\_conv2 (Conv2D) & (None, 14, 14, 512) & 2,359,808 \\ block5\_conv3 (Conv2D) & (None, 14, 14, 512) & 2,359,808 \\ block5\_conv4 (Conv2D) & (None, 14, 14, 512) & 2,359,808 \\ block5\_pool (MaxPooling2D) & (None, 7, 7, 512) & 0 \\ flatten (Flatten) & (None, 25,088) & 0 \\ dense (Dense) & (None, 512) & 12,845,568 \\ dropout (Dropout) & (None, 512) & 0 \\ dense\_1 (Dense) & (None, 4) & 2052 \\ \hline Total params: 32,872,004 & & & \\ Trainable params: 12,847,620 & & & \\ Non-trainable params: 20,024,384 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Layers of VGG19.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
**Hurricane** & 0.98 & 0.96 & 0.97 & 207 \\
**Earthquake** & 0.96 & 0.93 & 0.95 & 364 \\
**Flood** & 0.91 & 0.94 & 0.93 & 265 \\
**Wildfire** & 0.97 & 0.98 & 0.97 & 249 \\
**Accuracy** & & & 0.96 & 1107 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performance evaluation for the classification of severe weather events using VGG 19.