Localized Flood Detection With Minimal Labeled Social Media Data Using Transfer Learning
Table 2: Classification Accuracy Comparison
['[BOLD] Target Data', '[BOLD] Train Loss', '[BOLD] Test Loss', '[BOLD] Accuracy', '[BOLD] Time(sec)']
We have used the following hardware for the experimentation: Windows 10 Education desktop consisting of intel core i-7 processor and 16GB RAM. The pre-trained network was already trained and we used the target data Queensland flood which provided 96% accuracy with 0.118 Test loss in only 11 seconds provided we used only 70% of training labeled data. The second target data is Alberta flood with the same configuration of train-test split which provided 95% accuracy with 0.118 Test loss in just 19 seconds. As we can see it takes very less time to work with 20,000 of tweets (combined) and at times of emergency it can handle a huge amount of unlabeled data to classify into meaningful categories in minutes.