## 2 Literature Review
### Research Gaps and Motivation  
From the literature study described in Table 2, it is inferred that there are a variety of models for performing time-series and spatio-temporal analysis to predict extreme weather events with its own advantages and limitations. Some of the existing models fail to capture the dynamic nature of weather variables and the physical processes involved for prediction. Presently, the existing literature focuses on predicting the tropical cyclone intensity estimation using variants of convolutional neural networks. Additionally, several types of disasters such as earthquakes, floods, wildfire, hurricanes can be classified using the Deep Convolutional Neural Networks (DCNN) and the pre-trained models such as VGG (Visual Geometry Group), AlexNet, ResNet, InceptionNet. These models are utilized depending on the number of Convolutional layers that is framed for predicting the post-disaster damage incurred by the hurricane. This work mainly aims to improve the accuracy in predicting the tropical cyclone intensity and the classification of damage assessment caused by the disaster. As individual and citizen, it is important to understand how to protect ourselves from the hurricane. To reduce the hurricane risk, an improved hurricane accurate forecast is essential and since the limited resources are available for reducing the hurricane hazard, it is considered as an economic problem. Policy officials can analyze the effective investment in forecast generation for reducing the mortality and the property loss. Since hurricanes impose more threat to human lives and damages to properties, it is significantly important to understand the different stages of hurricane and its impact. Hurricane prediction helps to prevent the physical damage at various levels such as individual, business and at the society level. Determining an accurate tropical cyclone intensity values may help to provide early warning. The quality of the initial condition input to the model is significantly important in predicting the hurricane. Estimating the tropical cyclone intensity can help better initialization of the model. The forecast quality can be improved by considering the different aspects such as location of hurricane, intensity, forward speed, storm surge etc. The forecast users, providers and the policy makers can yield more information by evaluating the trade-off between the different aspects. In this study, we present an improved CNN model for estimating the tropical cyclone intensity and its categories effectively. In the proposed model, the max pooling layer is removed and the stride value of the previous convolutional layer is increased to improve the accuracy. Additionally, batch normalization is added after the first convolutional layer, which is used to normalize the input and improves the training time. The satellite image dataset and the corresponding wind speed data from HURDAT2 database are used together for training. Lower RMSE values are obtained using the proposed model when compared to the existing techniques. The model is generalized and is applicable for all the regions (Atlantic and Pacific). The model analyzes the images along with the wind speed data and provides the different categories of hurricanes with its vulnerability. Additionally, the post-disaster management and the classification of extreme weather events are carried out using the pre-trained models, and improved accuracy is obtained. The improved deep convolution neural network (I-DCNN) is used for hurricane intensity estimation. CNN with data augmentation and transfer learning using the pre-trained network VGG-19 is used for the classification of building damage assessment and for classifying the different types of weather events using a video dataset. The prediction results of the proposed models are compared with the existing models using various performance metrics. The novelty and the main contributions of the proposed study are as follows:  
* Exploratory data analysis and visualization of hurricanes is being carried out.