# Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning
## 4 Results and Discussion
### Model Training  
We present the transfer learning model training and validation metrics in this section. Validation occurs in the training process to evaluate the model's predictions on the validation dataset which contains images the model has not encountered during training. This gives an objective estimate of the model's accuracy and loss to compare to the training accuracy and training loss. Figure 3 shows the training metrics for flood damage classification using the cross-entropy loss and the accuracy defined in Equations (1) and (2), respectively. Figure 3a shows that the training loss and the validation loss using the ResNet model both converge to a value of approximately 0.5, indicating that the model does not experience overfitting or underfitting issues. On the other hand, training loss for the MobileNet model is around 0.05, while its validation loss is similar to that of ResNet, 0.5. Figure 3b shows training accuracy and validation accuracy for the two base models. Accuracy measures the ratio of correct prediction (including true _floods_ damage prediction and true _non-floods_ damage prediction) to the total number of predictions. The accuracy for the ResNet model converges to a value between 0.7 and 0.75. The accuracy using MobileNet is slightly different between training and validation. The training accuracy is close to 0.975, while the validation accuracy is about 0.9. Overall, the training metrics comparison shows that flood damage classification using the MobileNet model has a similar validation loss compared to that of ResNet, but it has a better accuracy. Figure 4 shows the training metrics for hurricane damage detection which utilize the cross-entropy, MSE, L1, and L2 loss functions in Equations (1), (7) and (8). Figure 4a shows that EfficientNet converges to an approximate value of 0.3, ResNet converges to an approximate value of 0.18, and MobileNet converge to the lowest value of training classification loss at approximately 0.05. All three models achieve values for classification loss \(\leq\) 0.3, which is generally accepted for concluding model training. However, ResNet experiences a sharp spike in classification loss between 0 and 500 epochs. This can be attributed to the early point in the training process where there has not been a significant relation of extracted features within the model's network to image features for the four classes in our model. Figure 4b shows that ResNet converges to a training localization loss of approximately 0.8, while MobileNet and EfficientNet both converge to an approximate value of 0.01 for training localization loss. EfficientNet also has a unique path of convergence for this parameter, as it begins the training process with a localization loss value of approximately 0.05 and finishes with a value of 0.01 which characterizes its loss curve to be constant relative to the loss curves for ResNet and MobileNet. This result can be attributed to the BiFPN structure of the EfficientDet model [27] which optimizes the accuracy of predictions, specifically bounding box regression in this case. Figure 4c shows that EfficientNet converges to an approximate value of 0.08, ResNet converges to an approximate value of 0.2, and MobileNet converges to the largest value of approximately 0.56 for regularization loss. The relatively high value for MobileNet can be attributed to less effective modeling of the regularization loss with the second term in both L1 and L2 of Equation (8). This term likely contributes a larger value for the MobileNet layer weights than the layer weights for EfficientNet and ResNet. Additionally, the MobileNet  
Figure 3: The training and validation cross-entropy loss and accuracy for the flood damage classification using ResNet and MobileNet. layer architecture lacks multiple two dimensional convolutions as implemented in the ResNet and EfficientNet layer architectures. The addition of supplemental convolution layers would likely further reduce overfitting in each model. Thus, the regularization loss would naturally decrease in accordance.