A New Method for Disaster-Affected Area Grade Zoning with Cloud Model
Yugang Tian, Donghua Qin, Wenbin Li · 2009
In order to focus on the serious area affected by earthquakes and other hazards, and to meet the demands of post-disaster reconstruction, the disaster-affected area must be evaluated scientifically. In general, select indicator set firstly, then construct comprehensive disaster index, finally divide the disaster-affected area into different grade zones according to a certain threshold determined by a total consideration. This kind of method is simple and easy to operate, but prone to be interfered by subjective factors. In response to this problem, a new evaluation method based on cloud model, one kind of uncertain artificial intelligence models, is proposed. The new method can obtain a "soft" grade zoning by directly learning from objective data set, the evaluation results are more scientific. In this paper, the authors take Wenchuan seismic disaster- affected grade zoning as an example to testify the feasibility and reliability of this new method.