A Forewarning Research on Individual Disaster-Affected Population of Disaster Emergency Management

The forewarning of disaster-affected population is an important issue related to the national economy development and national public security. To develop the research of the forewarning of disaster-affected population is the foundation of safeguarding public security and policy-making. This article is aimed at the characteristics of multiple uncertainty including fuzziness and random presented by disaster-affected population, proposing a new gray Markov GM(1,1) forewarning model by combining the advantages of both gray GM(1,1) model and Markov chain theory. Gray GM(1,1) model is used to imitate the original data of the individual disaster-affected population, then Markov chain theory is applied to presume the shifting rule among the conditions of disaster-affected population. Finally, the model is applied to make a forewarning analysis to the population hit by floods in a certain province. The results show that the new model has relatively high precision in the forewarning of individual disaster-affected population.