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

• Autorzy: Singla Annie , Agrawal Rajat , Linh Nguyen Thi Dieu

Identyfikatory

DOI

10.15439/2021KM66

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

convolutional neural networks; disaster management; long short-term memory; social media

PL

splotowe sieci neuronowe; zarządzanie katastrofami; pamięć krótkotrwała; media społecznościowe

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 28
• Strony: 273--275
• Opis fizyczny: Bibliogr. 10 poz., tab., il.

Twórcy

autor

Singla Annie

• Centre of Excellence in Disaster Mitigation & Management Indian Institute of Technology Roorkee, India

autor

Agrawal Rajat

• Department of Management Studies Indian Institute of Technology Roorkee, India

autor

Linh Nguyen Thi Dieu

• Science and Technology Department Hanoi University of Industry, Vietnam

Bibliografia

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Uwagi

1. Preface

2. Session: International Conference on Research in Management and Technovation