## 3 Methodology
### Methodology
#### 3.3.1 Hurricane Intensity Estimation
The attributes that denote the points in a three-dimensional space are latitude and longitude. The latitude and longitude coordinates are transformed into points, and the transition probabilities are calculated and added to the next row to determine the occurrence of the next location of the hurricane. Figure 3 shows the plot of the year and the maximum wind speed during different time periods. The heat or density maps are used to visualize the concentration of the feature in any specified area and are useful to identify the correlations between the features. Figure 4 shows the scatter plot of storms that occurred, and Figure 5 depicts the heat map with density contour until 2020. The data density can be depicted using the heatmap, and the correlation between the features are captured. For the spatial point data, the two dimensional kernel density estimation is used. The satellite images for the Atlantic and Pacific Oceans' hurricanes are downloaded for all the years, and the images are downloaded, to which the wind speed details are matched from the best track dataset from HURDAT2 database. The storms that do not contain the related information in the best track dataset are filtered. The satellite images from the netcdf files are extracted and stored locally. Since the hurricane is present at the center of all the images, the images are cropped at the center and matched with the maximum sustained wind speed of that hurricane. The best track dataset is filtered to search for the name, time, and date of the hurricane image. The matched images from the HURDAT2 best track data, which contains the records of Atlantic and Eastern/Central Pacific basins, are labeled with the wind speed and saved locally. The IR satellite image is retrieved from the file, the edges are removed, and the image is cropped with aside length of 50 pixels per inch (ppi). Figure 6 shows the architecture diagram for hurricane prediction by estimating the intensity of the tropical cyclone using satellite images. The cropped images are fed to the input layers, followed by a set of convolution operations and batch normalization with dropout. Data augmentation of hurricane images is done to train the data using the convolutional neural network. K-fold validation is used to validate the model, and the augmented images for each fold are generated and combined for training. If the tropical cyclone image is within the range of 50 to 70 knots, two new images are generated. On the other hand, six new images are generated if the tropical cyclone image is within the range of 75 to 100 knots, and 12 new images are generated if the range is greater than or equal to 100 knots. Figure 1: (**a**) Wind speed frequency; (**b**) storm events year-wise; (**c**) enlarged version of map; (**d**) occurrence of large storm. Figure 2: (**a**) Density of occurrence of storm; (**b**) point-wise density of occurrence of storm. Figure 4: Scatter plot of large storm counts. Figure 5: Density contour heat map. Figure 3: Storms with respect to time, year, and maximum wind speed. Keras Conv2D is a 2D convolution layer used to create a convolution kernel with input layers and produce the output tensors. The convolutional layers will learn from the number of filters, which is a mandatory parameter of 2D. The sequence of layers is input layers, convolution layer with activation function, pooling layers, and fully connected layers. Max pooling or average pooling is applied on the output of convolution operations to reduce the spatial dimension of the feature maps. It is used to get a small amount of translational invariance at each level. The problem associated with the pooling operation is that after applying several levels of pooling, the information about the precise positions will be lost. It is difficult to use the accurate spatial relationships between the higher-level parts of recognition.