(1.成都理工大学 a.信息工程学院;b.地球科学学院, 成都 610059;2.中石化西南油气分公司 勘探开发研究院 信息中心, 成都 610081)
Risk assessment of debris flow based on radial basis function neural network
CHEN Gang1a,2,HE Zhengwei1b,YANG Yang1a,YANG Bin1a
(1.a.College of Information Engineering, b.College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China;2.Information Center, Exploration & Production Research Institute, SWPB SINOPEC, Chengdu 610081, China)
Abstract:There were some nonlinear relation between main assessment indexes of debris flow and risk. Statistics analysis, fuzzy evaluation,BP network and other methods were usually adopted. However, they had some insufficiencies and they were difficult to accurately assess risk. In order to overcome insufficiencies of these methods, this paper combined with assessment indexes of debris flow and construct risk assessment model of debris flow based on radial basis function neural network (RBFNN).It also conducts a contrast of assesment result between the RBFNN model and BPNN.Experiment shows that, compared with BPNN, RBFNN simulation have higher data precision, less training time and closer to measurement data. Therefore, RBFNN is capable of making more precise risk assessment of debris flow after enough training, and it’s more valuable in application. ......