## 3 Methodology
### Methodology
#### 3.3.3 Hurricane Damage Prediction  
This section describes the convolutional neural network used to classify the building images obtained from satellites as damaged or undamaged. The damage assessment of hurricanes is important to identify the number of flooded or damaged buildings. The image classification algorithm, such as the convolutional neural network, is employed to improve the accuracy of damage prediction. The satellite image dataset of Hurricane Harvey in 2017 is considered for hurricane damage prediction, which contains attributes such as the path of the image, damage status, location, latitude and longitude, and many others. The satellite imagery dataset is divided into training, testing, and validation, and classified as flooded/damage and undamaged. In this section, fine-tuning of the pre-trained VGG 19 is used, which gives more accuracy for the damage prediction than the existing VGG 16 model. To assess the damage caused by the occurrence of an extreme weather event, manual inspection of the satellite imagery data is tedious and time-consuming. Therefore, optical sensor imagery data with computer vision and deep learning models will be useful for analyzing the hurricane damage, which can produce accurate results. Hence, the government and other stakeholders can take necessary actions and plan for the available resources. In the satellite imagery, the affected area will be automatically annotated as \"damage\" or \"no damage\". Machine learning and deep learning models are the main research focus in assessing the optical sensor imagery for post-disaster assessment. The satellite imagery data after Hurricane Harvey in the Greater Houston area are considered for training, and the images are obtained from the OpenStreetMap. Figure 9 shows the architectural diagram for hurricane building damage assessment and annotation. The reference network is loaded (VGG 19), and transfer learning takes place through which fine-tuning of the network structure is modified. The new weights are learned and updated by modifying the layers to suit our input dimension. By proper tuning of hyper parameters and optimization, the training speed can be improved [52]. For damage prediction, a total of 5000 images are used as training data for the no damage category and 5000 images are used for the damage category. For validation and testing, 1000 files for each category were chosen for prediction. Many activation functions such as Relu, tanh, sigmoid, softmax, exponential, soft sign, etc., are available, and the Relu activation function is used in the CNN and max pooling layers. In the output layer, the softmax activation function is used. The two target classes to be predicted include damage or no damage. The data is split into 70% for training and 30% for testing. Data preprocessing and data transformation are undertaken, and the labeled variables in the data are converted into a format that is understood by the model for training so that the prediction accuracy can be improved after normalizing the data. The images are cropped and resized before training, and the images are filtered to obtain the higher quality dataset. The Adam optimization algorithm is used to optimize and update the weights of the network, and training is done on the specified batch size. The errors are reduced and the performance is improved after executing every batch size. The loss function used in this  
Figure 8: Layered architecture for hurricane intensity estimation. study is mean square error (MSE), and epochs denote the number of iterations considered for training the dataset. The predictions are done on the test data after the training process is completed. For hurricane damage prediction, transfer learning can be done using feature extraction, where the network is considered an arbitrary feature extractor. Another way is to fine-tune the hyper parameters, where the weights are fine-tuned to recognize the new object classes.