摘要： 针对滑坡危险性预测中降雨等不确定因素难以获取,以及有效处理和标准反向传播算法存在局部极小值和训练速度慢等问题,为提高滑坡危险性的预测精度,提出一种不确定遗传神经网络滑坡预测方法。基于改进遗传算法和反向传播神经网络分类算法,结合滑坡灾害预测相关理论,考虑到与滑坡灾害密切相关的降雨等不确定因素,给出不确定数据分离度的概念,阐述不确定属性数据的处理方法,构建不确定遗传神经网络,建立滑坡灾害预测模型,以延安宝塔区为例进行验证。实验结果显示,该方法的有效精度和总体精度分别为92.1%和86.7%,验证了不确定遗传神经网络算法在滑坡灾害预测中的可行性。

关键词: 不确定数据, 滑坡, 遗传算法, 反向传播神经网络, 危险性预测

Abstract: Since the rainfall and other uncertainties are difficult to obtain and effectively deal with in landslide hazard prediction,and the existence of local minima and training slow in the standard back propagation algorithms,in order to improve the prediction accuracy,this paper proposes an uncertainty genetic neural network landslide prediction method.Based on modified genetic algorithm and back propagation neural network classification algorithm,combined with the landslide disaster prediction theory,taking into account the rainfall and other uncertainties in landslide,this paper proposes the concept of separation of uncertain data,elaborates the processing methods of uncertain property data,builds uncertain genetic neural network and the landslide hazard prediction model.It also selects Baota district of Yan’an study area to verify this method.Experimental results show that the effective accuracy and the overall accuracy of the proposed method are 92.1% and 86.7% respectively,which verifies the feasibility of uncertainty genetic neural network algorithm in landslide hazard prediction.

Key words: uncertain data, landslide, genetic algorithm, back propagation neural network, hazard prediction

中图分类号: 

• TP393.08