基于改进PSO-RBF的泥石流灾害预测

Prediction of Debris Flow Disaster Based on Improved PSO-RBF

我国幅员辽阔,环境多变,泥石流灾害频发,长期以来威胁着人民生命财产安全。开展泥石流灾害预警预测及防治工作,研究泥石流灾害预测模型是当前最紧迫的任务。针对现有研究模型存在的适用性不强和误差较大的问题,基于粒子群优化算法(Particle swarm optimization,PSO)对径向基函数神经网络(Radial basis function,RBF)进行改进,提出了一种泥石流灾害预测模型(PSO-RBF)。实验结果表明,相比单一的RBF模型,PSO-RBF模型在泥石流灾害预测中的预测精度更高、误差更小从而效果更好。本文的研究为泥石流灾害的研究提供了一种新的思路。

Our country is vast in territory with a changeable environment and frequent debris flow disasters,which have long threatened the safety of people’s lives and property.It is the most urgent task to carry out early warning,prediction and prevention of debris flow disasters,and to study debris flow disaster prediction models.Aiming at the problems of poor applicability and large errors in existing research models,the Radial Basis Function(RBF)is improved based on particle swarm optimization(PSO),and A debris flow disaster prediction model(PSO-RBF)is proposed.The experimental results show that compared with a single RBF model,the PSO-RBF model has higher prediction accuracy,smaller errors and better results in the prediction of debris flow disasters.The research in this article provides a new idea for the study of debris flow disasters.