## 6 Conclusions and Future Work  
In this work, the deep learning models are developed to estimate the tropical cyclone intensity and to identify the severity of different categories of hurricane. The conclusions of this exertion are summarized as follows:  
* For hurricane intensity estimation, an improved deep CNN model is trained with the satellite images of hurricane along with the wind speed data. The proposed model provides lower RMSE value of 7.6% and MAE value of 6.68% after removing the max-pooling layers, and adding the batch normalization after first convolution layer.
* For building damage assessment in context with post-disaster management, transfer learning using feature extraction of VGG 19 CNN achieves a higher accuracy of 98% than VGG 16 model, with most of the predictions as true positives.
* For classifying the severe weather events, fine-tuning of VGG 19 achieves a higher accuracy of 97% by training the video datasets
* Importance of mitigation measures against hurricane are addressed.  
Figure 23: Severe weather events detection and classification.  
The National Ocean and Atmospheric Administration (NOAA) and the National Hurricane Center (NHC) use different tools to predict the intensity of storms. For predicting the trajectory and strength of the hurricane, it is necessary to understand the structure of the storm and its location. Deep learning models help to accurately forecast the storm intensity at various levels, which helps the government to prevent hazards. To predict the hurricane damage prediction and the loss incurred is difficult, because it mainly depends on the storm severity and the vulnerability of the area the hurricane hits. Hurricanes also threaten the water and sewer systems, flood management, and transportation, in addition to the damages caused by the buildings. Strong hurricanes cause a high risk to public health and human lives, since within a shorter span of time, intensity changes may occur that may lead to human death and damages to property. The prediction of intensity levels using deep learning models provides an earlier warning of the storm formation. The potential future work includes adding a greater number of layers and improving the performance of the proposed model. The hurricane is located at the center of all the images, and there are various challenges related to data preprocessing, such as the removal of noise and distortion removal. It can be improved using efficient pre-processing techniques to enhance the efficiency of the prediction model. Additionally, severe weather events' impact across different countries should be controlled by designing the mitigation steps and including them in the development plan.  
The author [PERSON] developed the main theme of the article and performed the work on conceptualization, data curation, formal analysis, investigation, methodology, software, and writing--original draft; the author [PERSON] contributed to the conceptualization, data curation, investigation, methodology, software, supervision, validation, and review and editing; [PERSON] contributed to the investigation, methodology, validation, visualization, and review and editing; U.S. contributed to the review and editing of the article. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to thank the Department of Information Technology, Sri Venkateswara College of Engineering, Chennai, India. The authors would like to acknowledge the Prince Sultan University for supporting the article processing charges (APC) for this publication. The authors also thank the Clean and Resilient Energy Systems (CARES) Laboratory, Texas A&M University, Galveston, USA, for the technical expertise provided.  
The authors declare no conflict of interest.