基于PCA和改进CS-RBF的滑坡预报模型

Landslide Forecasting Model Based on PCA and Improved CS-RBF

滑坡灾害给人们的生命财产带来严重威胁,加强对滑坡灾害的有效预报具有重要意义。以陕西省山阳县研究区滑坡监测点为例,提出一种基于主成分分析算法(Principal Component Analysis,PCA)和布谷鸟算法(Cuckoo Search,CS)优化径向基神经网络(Radial Basis Function,RBF)的滑坡概率预测模型。首先确定该地区的滑坡灾害发生的主要影响因素,利用PCA算法将滑坡影响因子进行降维,避免数据维度过大,造成模型冗余的问题,将降维后的数据输入到RBF神经网络中进行滑坡概率预测;其次,利用改进的布谷鸟算法进行参数寻优,提高滑坡发生概率预测的准确性。并采用BP(Back Propagation)、RBF、GA-RBF(Genetic Algorithm-RBF)、CS-RBF等多种模型与改进CS-RBF模型进行对比实验,结果表明CS-RBF模型预测性能优于其他几种模型,其均方根误差为0.01756,平均绝对误差为0.01178,该模型可靠性更高,为滑坡预警的实际应用提供有力的支持和保障。

Landslide disasters pose a serious threat to human life and property,and strengthening effective forecasting of landslide disasters is of great significance.Taking the landslide monitoring points in Shanyang County,Shaanxi Province as an example,this study proposes a landslide probability forecasting model based on principal component analysis(PCA)and cuckoo search(CS)optimized radial basis function(RBF)neural network.Firstly,the main influencing factors of landslide disasters in the area are determined,and the PCA algorithm is used to reduce the dimensionality of landslide influencing factors to avoid the problem of model redundancy caused by excessively large data dimensions.The dimensionality-reduced data is then input into the RBF neural network for landslide probability forecasting.Secondly,an improved Cuckoo Search algorithm is used for parameter optimization to improve the accuracy of landslide probability forecasting.Various models including back propagation(BP),RBF,genetic algorithm-RBF(GA�RBF),CS-RBF,and others are compared with the improved CS-RBF model through experimental analysis.The results show that the predictive performance of the CS-RBF model is superior to the other models,with a root mean square error of 0.01756 and an average absolute error of 0.01178.This model exhibits higher reliability,providing strong support and guarantee for the practical application of landslide early warning.