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Comparing the effectiveness of Convolutional Neural Network and Long Short-Term Memory Network for Disaster Based Social Media Messages - Using Thunderstorm and Cyclone as Case Studies

Singla Annie, Agrawal Rajat, Linh Nguyen Thi Dieu

Annals of Computer Science and Information Systems

2021

Vol. 28

273--275

We present a framework to ameliorate the classification of disaster-related social media messages. In the present work, we have incorporated the Convolutional Neural Network, and Long Short-Term Memory Network. To demonstrate the applicability and effectiveness of the proposed approach, it is applied to the thunderstorm and cyclone Fani dataset. The results indicate that CNN is better than the LSTM model with an accuracy score of 0.9999 (99.99%) and loss score of 0.0410. The output from the research study is helpful for disaster managers to make effective decisions on time.