Evolution prediction of unconventional emergencies via neural network: An empirical study of megacities

Abstract

Megacities occupy an important position in the country they locate and even the whole world. Therefore it is of great significance to investigate the evolution mechanism of unconventional emergencies and to explore the probability of predicting the secondary disasters in megacities. In this paper we study the evolution prediction problem of unconventional emergencies and formulate it as multi-label classification. A novel multi-label learning vector quantization (LVQ) neural network is proposed to construct the prediction model able to forecast the type of sub-events. A real data set of 85 megacities all over the world is collected and used as a case study in the experiments. The prediction performance is measured by the match between the real label set and retrieved label set. The empirical results demonstrate the effectiveness of using LVQ in a multi-label scenario for predicting the type of secondary disasters. The model is beneficial for emergency managers to predict the potential secondary disasters and thus make informed decisions for disaster prevention and mitigation.