二维场地液化势预测的神经网络方法

Neural network approach for prediction of liquefaction potential of two-dimensional site

基于人工神经网络,提出了场地液化势预测模型。场地液化势的空间数据结构特征可由不同参数的自回归神经网络(GRNN)来模拟。该预测模型的一个重要参数spread可用地质统计学(Kriging)方法中的交叉验证技术来确定。研究表明,在最优spread参数条件下GRNN能够较好地映射场地液化势数据结构特征。用GRNN模型预测结果与经典的Kriging估计方法所得到的结果十分吻合。GRNN模型可以用于二维空间数据的预测及基于GIS决策系统。

The prediction model for liquefaction potential of site based on artificial neural networks is presented. The spatial data structural characteristics of liquefaction potential of site can be mapped by different spread values. The optimal spread value can be determined by means of cross-validation technique of geostatistics. Study results indicate that the generalized regression neural networks can map the structural characteristics of liquefaction potential under the condition of the optimal spread. The result obtained is consistent with that of Kriging estimator. The model can be applied to the prediction of two-dimensional spatial data and the decision system based on GIS.