Rapid assessment of flood loss based on neural network ensemble

基于神经网络集成的洪灾损失快速评估(英文)

Considering the defects of low accuracy and slow speed existing in traditional flood loss assessment, firstly, the technical route of flood loss assessment was presented based on the neural network ensemble. Secondly, through the study of certain country of Poyang Lake district, the flood loss assessment indicators of the test area were analyzed and extracted by utilizing analytic hierarchy process (AHP), and the weights of the impact factors were assigned. Subsequently, the approaches to generate individuals and conclusions of neural network ensemble model were also investigated. In the platform of C# language and neural network library under AForge.NET open source, a flood loss assessment program which could rapidly build neural network ensemble models was developed. Finally, the proposed method was tested and verified. The comparison results between the assessment results of the proposed method and the actual statistical flood loss proved the feasibility of this method, thus a new approach for flood loss assessment was provided.

针对传统洪灾损失评估存在精度低、速度慢等问题,首先提出基于神经网络集成的洪灾损失评估方法技术路线;其次,以鄱阳湖区某县为研究对象,运用层次分析法对影响该研究区域的洪灾损失评估指标进行分析和提取,并对影响因子的权重进行分配;然后,研究神经网络集成模型的个体生成和结论生成的实现方法,并利用C#编程语言和AForge.NET开源框架下的神经网络类库搭建一个能快速构建神经网络集成模型的程序;最后,对该方法进行应用,并将评估结果与实际统计的洪灾损失值进行对比分析,验证该评估方法的可行性,从而为洪灾损失评估提供一种新的方法。