基于BP神经网络的宁波市台风灾情预估模型研究

选取1949—2015年间对宁波市影响较大、灾情记录完整的58个台风样本,基于灾损数据,采用灰色关联分析法建立台风灾情关联度,选取台风灾害致灾因子、台风灾情综合关联度,利用BP神经网络建立台风灾情预估模型。结果表明,利用台风灾情关联度评估台风灾情大小合理可用,台风灾害致灾因子与灾情评价指标及台风灾情综合关联度间均存在一定的相关性,利用BP神经网络预估模型对台风灾情预估效果较好,其中训练样本、测试样本的模拟值与实际值相关系数分别达到0. 94、0. 865,均通过了0. 01信度的显著性检验,训练集、测试集灾情级别预报一致率为85. 3%、77. 8%,相关研究成果可为政府决策部门的抗台减灾工作提供科学依据。

Investigate on the pre-assessment of typhoon disaster in Ningbo based on BP neural network

Expending 58 typhoon cases that had the considerable effect on Ningbo and had finish catastrophe records from 1949 to 2015. In view of the information of the calamity, the comprehensive correlation degree of typhoon disaster (R_oj) was set up by utilizing the grey relational investigation technique. Choosing the disaster-causing factors of typhoon and R_oj that point build disaster pre-assessment technique of typhoon disaster by utilizing BP neural network (BP). The outcomes demonstrated that, the severity of typhoon which evaluated by R_oj is reasonable and available. There is a significant correlation between typhoon disaster risk factors and disaster assessment indicators as well as R_oj. The pre-evaluation model of BP is useful for predicting typhoon disaster;the correlation coefficient linking the simulated value and the actual value of the training set and the test set respectively reached 0.94 and 0.896 and both achieved the confidence interval of 0.01. The consensus rate of the disaster level forecast of the training set and the test set is 85.3% and 77.8% respectively. This investigate outcomes could provide scientific premise to counter the typhoon work of government decision-making divisions.