Computer and Modernization ›› 2020, Vol. 0 ›› Issue (09): 95-99.doi: 10.3969/j.issn.1006-2475.2020.09.017

Previous Articles     Next Articles

Prediction of Debris Flow Based on Improved Extreme Learning Machine

  

  1. (1. School of Civil Engineering, Lanzhou University of Technology, Lanzhou 730050, China;
    2. School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China)
  • Received:2020-02-02 Online:2020-09-24 Published:2020-09-24

PDF (PC)

836

Abstract: 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.

Key words: debris flow, extreme learning machine, DBSCAN, prediction

CLC Number: