谢静峰, 谭飞, 焦玉勇, 等. 2021. 基于因子分析的GA-ELM模型岩溶地面塌陷预测[J]. 工程地质学报, 29(2): 236-544. doi: 10.13544/j.cnki.jeg.2020-219.
    引用本文: 谢静峰, 谭飞, 焦玉勇, 等. 2021. 基于因子分析的GA-ELM模型岩溶地面塌陷预测[J]. 工程地质学报, 29(2): 236-544. doi: 10.13544/j.cnki.jeg.2020-219.
    Xie Jingfeng, Tan Fei, Jiao Yuyong, et al. 2021. Prediction of karst ground collapse based on factor analysis-GA-ELM model[J]. Journal of Engineering Geology, 29(2): 236-544. doi: 10.13544/j.cnki.jeg.2020-219.
    Citation: Xie Jingfeng, Tan Fei, Jiao Yuyong, et al. 2021. Prediction of karst ground collapse based on factor analysis-GA-ELM model[J]. Journal of Engineering Geology, 29(2): 236-544. doi: 10.13544/j.cnki.jeg.2020-219.

    基于因子分析的GA-ELM模型岩溶地面塌陷预测

    PREDICTION OF KARST GROUND COLLAPSE BASED ON FACTOR ANALYSIS-GA-ELM MODEL

    • 摘要: 岩溶地面塌陷是多种因素共同作用的结果,具有隐蔽性和突发性的特点。为了快速、准确地对岩溶地面塌陷进行预测,提出了一种在因子分析的基础上利用遗传算法(GA)优化的极限学习机(ELM)岩溶地面塌陷预测模型。选取8个岩溶地面塌陷影响因素,利用因子分析提取5个公因子,然后输入GA-ELM模型进行预测。利用20组实例作为样本进行学习预测,以其中12组作为训练集,另外8组作为测试集。结果表明:进行因子分析后,不仅使ELM模型网络结构进一步简化,还提高了在相同隐含层神经元节点数情况下的预测正确率;在样本数较少的情况下,可以通过提高隐含层神经元节点数的方法来提高ELM模型预测正确率;GA-ELM模型相对于ELM模型的预测正确率明显提高,其具有更强的学习、预测能力;基于因子分析的GA-ELM岩溶地面塌陷预测是一种简单、准确、高效的方法。

       

      Abstract: Karst ground collapse is the result of many factors, and has the characteristics of concealment and abruptness. In order to predict karst ground collapse quickly and accurately, this paper presents a karst ground collapse prediction model which is based on factor analysis, genetic algorithm(GA) and extreme learning machine(ELM). Firstly, we identify eight typical influencing factors in the karst area. Then, we extract five common factors by factor analysis. Finally, we input the common factors into GA-ELM model and predict karst ground collapse. The sample data contains 20 groups of actual case data, of which 12 groups as training sets and the other 8 groups as test sets. The conclusions are as follows: Factor analysis can not only simplify the ELM model network structure, but also improve the prediction accuracy under the same number of hidden layer of neuron nodes. In the case of a small number of samples, the prediction accuracy of the ELM model can be improved by increasing the number of hidden layer neuron nodes. Compared with the ELM model, the prediction accuracy of GA-ELM model is improved significantly, with stronger learning and prediction ability. The prediction method of karst ground collapse based on Factor Analysis-GA-ELM model is simple, accurate and efficient.

       

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