## 3 Methodology
### Methodology
#### 3.3.1 Hurricane Intensity Estimation
In this study, for hurricane intensity estimation, the pooling layer is replaced by a higher stride operation in the previous convolutional layer to speed up the training and reduce the computational time. In the proposed methodology, the batch normalization layer is added after the first 2D convolution layer. The accuracy is improved by removing the max-pooling layers, and the 2D convolution layers are followed by the dropout, fully connected layers with ReLU activation, and the output layer with no activation function. Table 3 represents the different categories of hurricanes with respect to wind speed range in miles/h. To reduce the loss metric value, the optimizer learns the new weights for the model. RmsProp and Adam optimizers are used to configure the model and to modify with the new weights. The mean absolute error (MAE) values are computed by plotting the training and testing loss. The different categories of hurricanes such as tropical depressions, tropical storms, and category 1, 2, 3, and 4 are plotted. Dropout and batch normalization techniques are used to address the challenges in learning a neural network. To reduce the over-fitting of data, the regularization techniques are used, and dropouts are used to modify the network architecture randomly. For better results, dropout requires fine-tuning of the hyper-parameters. For optimizing the learning \begin{table}
\begin{tabular}{c c} \hline \hline
**Hurricane Category** & **Sustained Wind Speed Range (miles/h)** \\ \hline Tropical Depression & \textless{}=33 \\ Tropical Storm & 34–64 \\ Category 1 & 74–95 \\ Category 2 & 96–110 \\ Category 3 & 111–130 \\ Category 4 & 131–155 \\ Category 5 & \textgreater{}155 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Categories of hurricane. Figure 6: Architecture diagram for hurricane intensity estimation using improved CNN. of the neural network, Adam and RMSProp are used, which are the most commonly used optimizers in Keras. Adam requires more hyper tuning, and RMSProp needs minimal tuning of parameters. Dropout increases the training convergence time, whereas the batch normalization technique helps the model to converge faster and is used to improve the efficiency of the model training. The batch normalization process improves the training time by normalizing the input at each layer. In the proposed architecture, batch normalization is added only after the first 2D convolution layer. The HURDAT2 best track data contains the input features such as storm id, storm name, year, latitude and longitude at the center, and the maximum sustained wind speed. The image with the corresponding wind speed value in the database is fed as an input to the neural network. The features are trained by the first 2D convolution layer and 32 filters with the input shape of 50 \(\times\) 50. Then, batch normalization normalizes the input values followed by two 2D convolution layers with 64 filters. The output of the convolutional layers is then fed to the fully connected dense layers, and the target variable is the intensity estimation of the hurricane. Based on the intensity values, the storms are grouped into different categories, and the severity of the disaster is identified by classifying the images.