Study on typhoon disaster assessment by mining data from social media based on artificial neural network

Abstract

Typhoon disaster is a major threat to the economy and personnel safety in coastal areas. After the disaster, the objective assessment of typhoon disaster economic losses can provide an important reference for the post-disaster rescue and reconstruction and improve the scientific decision making. In this study, social media data, disaster causing factors, disaster bearing carriers (exposure and vulnerability) and other factors in traditional disasters were combined to achieve rapid disaster assessment. Convolutional neural networks were used to train a text classifier and perform automatic text classification of social media data. The correlations were discussed between various texts and disaster economic losses. It was found that there was a strong correlation between the geographical distribution of texts describing disaster damage and disaster economic losses. A back-propagation neural network was used for supervised learning to realize disaster loss assessment. To prove the reliability and applicability of the evaluation model, typhoons “Mangkhut” and “Lekima” were selected as study cases to realize the whole process from information collection to final assessment. The introduction of social media data modified the assessment results obtained by traditional methods and reduced the difference between the estimated disaster loss value and the actual value.