Abstract
Natural calamities are ineluctable and have caused severe damage to both life and property. The rate of the occurrence of natural disasters has been drastically increasing over time. It takes a long time to abate the impact of these calamities. One such natural disaster, the flood has caused a severe impact on the lives of people. Chennai, the capital city of Tamil Nadu is frequently affected by floods due to heavy monsoon rains, especially in recent years. Under these difficult circumstances, social media platforms like Facebook and Twitter have played a veritably huge part in the communication of people. This paper proposes Deep Learning models and Natural Language Processing (NLP) models to classify social media posts and perform spatiotemporal modelling for finding the exact time and location of flood affected areas to examine the flood severity and take immediate recovery response. The proposed methodology, DistilBERT outperforms other traditional models as most of them are keyword based which limits the scope of the classification. To overcome the disadvantages faced by geotag based approaches, Named Entity Recognition (NER) is proposed to find the location. Both the models are trained using social media feeds posted during the Chennai floods. The accuracy obtained by the proposed methodology is 99% for text classification (DistilBERT) and 89% for location identification (NER). The results obtained imply that the proposed methodology can be an efficient approach for disaster management.
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Gokul Raj, S., Chitra, P., Silesh, A., Lingeshwaran, R. (2023). Flood Severity Assessment Using DistilBERT and NER. In: Singh, P., Singh, D., Tiwari, V., Misra, S. (eds) Machine Learning and Computational Intelligence Techniques for Data Engineering. MISP 2022. Lecture Notes in Electrical Engineering, vol 998. Springer, Singapore. https://doi.org/10.1007/978-981-99-0047-3_34
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DOI: https://doi.org/10.1007/978-981-99-0047-3_34
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