鄢好, 李绍红, 吴礼舟. 2019: 联合多种数据驱动建模方法的滑坡位移预测研究. 工程地质学报, 27(2): 459-465. DOI: 10.13544/j.cnki.jeg.2017-485
    引用本文: 鄢好, 李绍红, 吴礼舟. 2019: 联合多种数据驱动建模方法的滑坡位移预测研究. 工程地质学报, 27(2): 459-465. DOI: 10.13544/j.cnki.jeg.2017-485
    YAN Hao, LI Shaohong, WU Lizhou. 2019: LANDSLIDE DISPLACEMENT PREDICTION BASED ON MULTIPLE DATA-DRIVEN MODEL METHODS. JOURNAL OF ENGINEERING GEOLOGY, 27(2): 459-465. DOI: 10.13544/j.cnki.jeg.2017-485
    Citation: YAN Hao, LI Shaohong, WU Lizhou. 2019: LANDSLIDE DISPLACEMENT PREDICTION BASED ON MULTIPLE DATA-DRIVEN MODEL METHODS. JOURNAL OF ENGINEERING GEOLOGY, 27(2): 459-465. DOI: 10.13544/j.cnki.jeg.2017-485

    联合多种数据驱动建模方法的滑坡位移预测研究

    LANDSLIDE DISPLACEMENT PREDICTION BASED ON MULTIPLE DATA-DRIVEN MODEL METHODS

    • 摘要: 边坡位移是滑坡演化的宏观体现,分析并预测滑坡位移发展态势对于防灾减灾具有重要意义。由于滑坡位移曲线具有明显的非线性特征,单一模型往往难以刻画其非线性与复杂性。为发展一种普遍适用于滑坡位移的预测方法,提出了一种联合多种数据驱动模型的新方法。该方法根据时间序列分析理论,将滑坡位移序列分解为趋势项和周期项,趋势项采用并联型灰色神经网络处理,周期项则采用人工蜂群算法(ABC)优化后的极限学习机模型(ELM)处理,从而充分应用各种模型的优点。以三峡库区白水河和八字门滑坡为例,对位移数据进行分析处理后,灰色神经网络模型预测其趋势性位移,改进后的极限学习机模型对周期性位移进行训练及预测。结果表明:在预测精度上,优化后的极限学习机模型准确度高于极限学习机模型及小波神经网络等方法,提出的灰色神经网络与ABC-ELM的组合模型可作为实际工程的一个参考。

       

      Abstract: Slope displacement is a macroscopic manifestation of landslide evolution. Analyzing and predicting landslide displacement are of great significance for disaster prevention and mitigation. Since the landslide displacement is obvious nonlinear characteristics, single model is often difficult to delineate the complexity and nonlinearity of landslide displacement. To find a universal method for predicting landslide displacement, a new method combined with multiple data-driven modeling methods is proposed to predict the landslide displacement. The new method is based on time series analysis. The landslide displacement sequence is decomposed into trend term and periodic term. The trend term is treated using parallel grey neural network, and uses the artificial bee conony(ABC)to find the optimal extreme learning machine model(ELM)to predict the periodic term. This paper takes Baishuihe landslide and Bazimen landslide for examples. After statistically analyzing the displacement data, the gray neural network model predicts trending displacement, and the optimized learning machine model train and predict the periodic term. The result shows that the optimized extreme learning machine model is better than the extreme learning machine model and wavelet neural network. Therefore, the proposed combination of grey neural network and ABC-ELM can be used as a reference for practical engineering.

       

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