摘要
为提高泥石流预测预报的准确性,提出一种基于DBSCAN聚类的改进极限学习机(ELM)算法。首先,利用DBSCAN算法对泥石流发生训练的数据进行聚类处理;其次,将聚类得到的不同训练集分类训练ELM分类器;最后,利用ELM分类器对预测集数据进行预测。实验结果表明,利用改进ELM算法对泥石流发生预测的平均准确率达到91.6%,改进ELM算法的稳定性与传统ELM算法相比有明显提高,与传统ELM算法、BP神经网络和Fisher预测法相比,改进ELM算法的预测精度更高。
In order to improve the accuracy of debris flow prediction,an improved Extreme Learning Machine(ELM)algorithm based on DBSCAN clustering is proposed in this paper.Firstly,DBSCAN algorithm is used to cluster the training data about debris flow.Secondly,the ELM classifier is trained by classifying the different training sets obtained by clustering.Finally,the ELM classifier is used to predict the data of the prediction sets.The experimental results show that the accuracy of the improved ELM algorithm in predicting the occurrence of debris flow is 91.6%on average.Compared with the traditional ELM algorithm,the stability of the improved ELM algorithm is significantly improved.Compared with the traditional ELM algorithm,BP neural network and Fisher prediction method,the improved ELM algorithm has higher prediction accuracy.
作者
曾鼎
曾勇
ZENG Ding;ZENG Yong(School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China;School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China)
出处
《计算机与现代化》
2020年第9期95-99,共5页
Computer and Modernization
基金
四川省科技计划重点研发项目(2019YFG0460)。
