## 1 Introduction
Because of the high complex nature of the earth's atmosphere, it is very challenging for the researchers to simulate a 100% accurate model [5]. Spatio-temporal relational probability trees are used for capturing spatio-temporal relationships between weather variables and improving the prediction accuracy of severe weather events [6]. Predicting the storm's lifetime is important to provide early warnings and to take preventive measures. Random forest (RF) classification and regression models predict the closeness of the observed storm with the forecasted storm \begin{table}
\begin{tabular}{c c c} \hline \hline
**Region** & **Development** & **Sustained Winds (km/h)** \\ \hline \multirow{4}{*}{Tropical} & Tropical Storm & Between 64 km/hour to 118 \\  & & km/h (74 miles/h) \\ \cline{2-3}  & Hurricane & \textgreater{} or = 119 km/h (74 miles/h) \\ \cline{2-3}  & Tropical Depression & \textless{} or = 63 km/h (39 miles/h) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hurricane classification with respect to wind range. and also capture the correlation between the parameters of the data. Machine learning and deep learning can be applied to a variety of high-impact weather applications, and good prediction results can be achieved [7]. Worldwide, more than 100,000 deaths can occur due to hurricanes, and a single event may cause up to 1000 deaths. Large economic losses are incurred, and even $100 billion damage is caused by some storms. Hence, there is a pressing need to accurately predict the intensity of the tropical cyclone for disaster preparedness. Satellite measurements are used for predicting the intensity values. The Dvorak technique was used to estimate the intensity with human interpretation while direct measurements are not available. Based on the cloud features such as length and the curvature of storm outer bands, the intensity is estimated by capturing the relationship between the features [8]. In the advanced Dvorak technique, the passive microwave data and the measurements of the aircraft are included to estimate the intensity, but still lower performance is achieved for the weaker storms [9; 10]. The deviation angle variation technique uses infrared satellite imagery and computes the gradient vector, and in this case, the variance is used to estimate the intensity values. However, the center of the tropical cyclone images should be clearly marked, and it is difficult to fit the parameters over multiple regions [11; 12]. A passive microwave-imagery-based technique was used to estimate the intensity by capturing the inner structure and the details of the cyclone, but the resulted accuracy was less. The deviation angle variation technique is widely used to estimate the intensity in north Atlantic and Pacific oceans [13; 14]. When compared to infrared satellite images, the microwave measurement data have significantly less temporal frequency of observations. Many machine learning (ML) models are capable of learning the data directly without depending on the mathematical equations or models. Recently, ML models have provided good accuracy in predicting the weather events. Modern artificial intelligence (AI) techniques are used for high-impact weather forecasting to extract the features from the weather data and are useful for real-time decision making processes [15]. The main hazardous attributes of hurricanes are winds, rainfall, and storm surges. Destruction can be caused because of the impact of the sustained wind range and can cause damage to the agricultural crops. Tall buildings can shake and fall down, and also because of the pressure differences, the roofs and the buildings are also affected and damaged. ML model predictions are dependent on some set of criteria or based on the value of the threshold. However, the weather variables of the extreme events are dynamic in nature, and spatio-temporal correlation must be identified. ML models cannot produce accurate results in identifying the spatio-temporal relationship between the variables.