基于主成分分析的PSO-ELM与Adaboost算法耦合模型在极震区泥石流物源动储量计算中的应用
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P 642.2

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国家创新研究群体科学(41521002),高等学校博士学科点专项科研(20135122130002),四川省国土资源科研项目(KJ-2018-17)。Supported by Funds for Creative Research Groups of China(Grant No. 41521002) , Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20135122130002) and Research Project of Land and Resources in Sichuan Province of China(Grant No. KJ-2018-17).


The Calculation Method For Predicting Dynamic Reserve in Meizoseismal Area Based On a coupling model between PSO-ELM and AdaBoost by PCA
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    摘要:

    针对极震区泥石流物源动储量受到多种因素的影响呈现出非线性特征不易计算的问题,提出了一种基于主成分分析(PCA)的粒子群优化极限学习机与AdaBoost算法耦合(PSO-ELM_AdaBoost)的计算模型。以汶川地震极震区区内的60条泥石流样本,从泥石流物源形成与启动方式入手,考虑沟域面积、相对高差、主沟长度、距发震断裂带距离、沟床平均纵比降和物源静储量六种泥石流物源动储量影响因子。运用Person相关性系数(PCC)、灰色关联度(GRG)和最大互信息系数(MIC)对影响因子进行敏感性分析,验证选取因子合理性;基于PCA对样本数据进行降维,避免信息冗余;使用PSO-ELM_AdaBoost耦合模型对处理后的样本数据进行训练和预测,并将结果与BP、SVM、ELM和PSO-ELM模型计算值进行比较;为验证模型适宜性,从每个子研究区中抽取一条泥石流沟和其他极震区的三条泥石流沟应用PSO-ELM_AdaBoost模型进行泥石流物源动储量计算。结果表明,该模型计算精度优于传统计算模型和其他神经网络模型,具有较好的适宜性和稳定性,是一种可靠的极震区物源动储量计算方法,能够为泥石流防治工程的设计提供有价值的参考。

    Abstract:

    The dynamic reserve in meizoseismal area is influenced by many factors and embody nonlinear characteristics, which is difficult to estimate. Under this circumstance, a coupling model between particle swarm optimization, extreme learning machine and AdaBoost algorithm(PSO-ELM_AdaBoost) is proposed based on principal component analysis(PCA). Taking 60 groups of debris flow samples from the Wenchuan meizoseismal area, this paper selects six influencing factors including: the gully’s area, relative height difference, the main channel length, the distance from seismogenic fault zone, the mean gradient and the total amount of material source based on the source formation and starting mode of the debris flow. First, Pearson Correlation Coefficient(PCC), Grey Relational Grade(GRG) and Maximal Information Coefficient(MIC) were used to analyze the sensitivity and response of the impact factors, so as to verify the rationality of the selection factors. Secoudly, the sample data are dimensionality reduced based on PCA to avoid information redundancy. Then, the PSO-ELM_AdaBoost coupling model is used to train and predict the processed sample data and compare the results with the estimated values of BP, SVM, ELM and PSO-ELM models. Finally, in order to verify the suitability of the model, A test sample set, which is obtained by extracting a sample from each sub-research area and three samples from other meizoseismal area, use the PSO-ELM_AdaBoost coupling model to calculate. The results show that the coupling model has higher accuracy and better versatility and stability compared with traditional calculation model and other neural network models. It can provide valuable reference for the design of debris flow prevention and control.

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李桥,巨能攀,黄健,等. 基于主成分分析的PSO-ELM与Adaboost算法耦合模型在极震区泥石流物源动储量计算中的应用[J]. 科学技术与工程, 2020, 20(15): 5961-5970.
Li Qiao, Ju Nengpan, Huang Jian, et al. The Calculation Method For Predicting Dynamic Reserve in Meizoseismal Area Based On a coupling model between PSO-ELM and AdaBoost by PCA[J]. Science Technology and Engineering,2020,20(15):5961-5970.

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历史
  • 收稿日期:2019-08-29
  • 最后修改日期:2019-10-31
  • 录用日期:2019-11-11
  • 在线发布日期: 2020-06-24
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