## 3 Methodology
### Methodology
#### 3.3.1 Hurricane Intensity Estimation  
This section provides the details of architecture used for hurricane intensity estimation. Caribbean hurricanes are the most frequently occurring extreme weather event that impacts the Caribbean. This occurs especially due to the large volume of humidity and warm air, which are measured by the power dispersion index (PDI) and Saffir-Simpson scale. The hurricane season in the Greater Caribbean region lasts from June to November, and 85% of the hurricanes occur during the period of August and September. Upon monitoring 100 tropical depressions, it is observed that six out of 10 tropical storms turns into hurricanes every year on average. The most seasonal variability always occurs at the Atlantic Basin. The catastrophic category of damage occurred during the 2017 hurricane season in the Caribbean islands. It led to a significant loss of human life and damage to property, and the hurricane damage assessment after the disaster was carried out. The expected property damage can be predicted using the synthetic hurricane tracks [47]. Using the disturbance index, the detection of droughts and hurricane damage on the Caribbean islands is accomplished. After the hurricane Maria, a strongest hurricane hit Puerto Rico; it took 2.5 months for the recovery after the disaster [48]. The coastal regions are mostly affected by the severe storms that cause damage to human lives and property. Based on the coastal properties, the average return period is ten years for the damaging storm, and 2 percent of the storm occurrence causes destructive damages. Additionally, most of the damages are due to the storm surge and storm winds [49]. The severe hurricane rainfall events that affected the Caribbean region are due to the influence of 2 \({}^{\circ}\)C of global warming. The vulnerability can be assessed using three factors: susceptibility, lack of coping capacities, and lack of adaptation. These factors are used to identify the most critical areas and the crucial variables that lead to vulnerability in any specific area [50]. The total number of storms that occurred is 229 over a period of 65 years. The average wind speed over 65 years is 3.52 knots. The most popular programming language, Python, is used for the implementation of this study. Python has a collection of pre-built libraries for image processing, scientific computing, machine learning, deep learning, data analytics, and data visualization. Complex data visualizations can be made using libraries, and visual data models can be easily created, which can help the analysts to discover the hidden patterns in the data. The libraries used for data visualization are Matplotlib, Plotly, Seaborn, GgPlot, and Geopltiblib. Matplotlib is used to embed the various kinds of plots such as scatter plots, error charts, bar charts, pie charts, histograms, etc., into the applications. Plotly provides additional plots such as contour plots, which are not present in the other data visualization libraries. Seaborn, which is based on Matplotlib, is integrated with the data structures such as Numpy and Pandas. Additionally, it provides various plots and the color palette for extracting the patterns in the data. Ggplot uses a high-level application programming interface (API) for creating the plots and isdeeply integrated with Pandas. Geopltiblib is used to create the geographical maps such as dot-density maps, symbol maps, etc., and is widely used for geographical visualizations. Keras, a deep learning framework that is built on top of TensorFlow, is used for implementation to scale a large cluster of graphical processing units (GPUs). Figure 1a shows the bar graph of the month-wise occurrence of storms, and Figure 1b represents the plot of the year and the number of storm counts with respect to the average wind speed frequency for the Caribbean's large storm. Figure 1c,d and Figure 2a,b depict the location of the occurrence of the hurricane and the enlarged version of the map generated using Geopandas.