## 5 Results and Discussions
### Detection and Annotation of Severe Weather Events  
This section discusses the detection and annotation of different types of severe weather events using the pre-trained network. The dataset was collected from the PyimageSearch with a total of 4400 Google images of different weather events. The dataset is divided into 75% for training, 25% for testing, and 10% for validation from the training split. Classification using video streams of weather events is tested and demonstrated. Figure 20 shows the learning rate with the maximum and minimum bounds. The optimal learning rate is found between \(1\times 10^{-6}\) to \(1\times 10^{-4}\), since the loss value drops until \(1\times 10^{-4}\) and starts increasing after that, which leads to over-fitting. After fine-tuning the last few layers, the accuracy of the model is increased, and 97% accuracy is achieved in detecting the severe weather events. Figure 21a shows that the validation loss follows the training loss, implying that there is very little over-fitting in the original dataset itself. Figure 21b shows the accuracy graph. Figure 22 shows the learning plot where the cyclical learning rate (CLR) callback is depicted between the optimal range of LR test. The trained model is tested over images and videos of weather events, which have similar semantic contents. The predictions queue, video streams, and the frame dimensions are initialized. The frames of the video streams are stored and resized to the fixed size of 224 \(\times\) 224. The automatic annotation of the frame is done by extracting the highest probability class label for the predictions in the queue. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref** & **Disaster Type** & **Dataset** & **Model Used** & **Accuracy** \\ \hline
[54] & Hurricane & Hurricane satellite images & Deep CNN & 80.66\% \\ \hline
[55] & Flood & Flood video dataset & Base CNN & 70\% \\ \hline
[56] & Hurricane & AIDR dataset & VGG-16 & 74\% \\ \hline
[57] & Hurricane & Hurricane Sandy dataset & Convolutional Auto-encoders & 88.4\% \\ \hline
[58] & Hurricane & Hurricane Harvey & VGG 16 CNN & 89.5\% \\ \hline
**Proposed** & **Hurricane** & **Hurricane Harvey** & **VGG 19 CNN (with** & **98\%** \\  & & & **fine-tuning)** & **98\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparative analysis of existing models. Figure 20: Minimum and maximum bound learning rate values. Figure 21: (**a**) Epochs vs. loss plot. (**b**) Epochs vs. accuracy plot. Table 10 shows the layer type, output shape, and the number of parameters in VGG 19 layers. The input image is processed by the convolutional layers, followed by nonlinear transforming unit ReLU and a pooling layer. The low-level features can be extracted in the deep learning models through down sampling, which can be achieved by the pooling layers. ReLU is applied to the feature maps generated by the convolutional layers. The pooling layer operates on each feature map and generates a new set of pooled feature maps. The pooling layer will reduce the size of the feature map by a factor of 2, and max pooling computes the maximum value in the set of feature maps. The total number of parameters, the number of trainable and non-trainable parameters using VGG 19, is shown in the table. Table 11 shows the performance evaluation of various metrics for classifying different types of weather events. Figure 23 shows the detection of severe weather events with the annotation of video clips in real-time. The rolling frame classification technique is used, where the rolling average forecast is computed using the standard average. The first calculated average is the simple standard average, and the forecast after the first standard average forecast is called the rolling average prediction. It denotes the process of moving the average value to the next set of n periods of frames. In the datasets collected, there is temporal and semantic similarity between the frames of the video streams and so the average rolling classification approach provides good results.