| 地震黄土滑坡滑距预测的BP神经网络模型 |
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| 引用本文： | 常晁瑜,薄景山,李孝波,乔峰,闫东晗.地震黄土滑坡滑距预测的BP神经网络模型[J].西北地震学报,2020,42(6):1609-1614. |
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| 作者姓名： | 常晁瑜 薄景山 李孝波 乔峰 闫东晗 |
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| 作者单位： | 防灾科技学院, 河北 三河 065201;中国地震局工程力学研究所, 中国地震局地震工程与工程振动重点研究室, 黑龙江 哈尔滨 150080 |
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| 基金项目： | 地震星火计划（XH18071Y）；国家自然科学基金项目（51808118；51608118） |
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| 摘 要： | 地震滑坡的滑距与重力滑坡的滑距有着显著的不同，科学预测地震发生时黄土地区滑坡的滑动距离是合理评估黄土地区滑坡风险和减轻滑坡灾害的有效方式之一。基于海原特大地震诱发黄土滑坡的400组野外调查数据，通过引入BP神经网络算法，论证了BP神经网络模型用于预测黄土地震滑坡滑距的适宜性和可行性；建立了地震诱发黄土滑坡滑距的BP神经网络预测模型，并通过67组数据进行了验证。BP神经网络算法和传统多元线性回归、多元非线性回归结果的对比显示，BP神经网络的预测更接近真实情况，具有较为理想的预测效果，可以用于黄土地震滑坡滑距的预测，并为圈定较为可靠的致灾范围提供依据。
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| 关 键 词： | 黄土地震滑坡 滑距 BP神经网络模型 滑距预测 |
| 收稿时间： | 2018/11/26 0:00:00 |
A BP Neural Network Model for Forecasting Sliding Distance of Seismic Loess Landslides |
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| CHANG Chaoyu,BO Jingshan,LI Xiaobo,QIAO Feng,YAN Donghan.A BP Neural Network Model for Forecasting Sliding Distance of Seismic Loess Landslides[J].Northwestern Seismological Journal,2020,42(6):1609-1614. |
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| Authors: | CHANG Chaoyu BO Jingshan LI Xiaobo QIAO Feng YAN Donghan |
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| Institution: | Institute of Disaster Prevention, Sanhe 065201, Hebei, China;Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of EngineeringMechanics, China Earthquake Administration, Harbin 150080, Heilongjiang, China |
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| Abstract: | The sliding distance of an earthquake landslide is significantly different from that of a gravity landslide. Scientific prediction of the sliding distance of seismically-induced landslides in loess regions is an effective way to reasonably assess the risk and minimize the hazards of such landslides. Based on 400 groups of field survey data of loess landslides triggered by the 1920 Haiyuan earthquake and 67 sets of verification data, feasibility and suitability of the back propagation (BP) neural network model for predicting sliding distances of seismic landslides was demonstrated. Comparison of the results of BP neural network algorithm with those of traditional multiple linear regression and multiple nonlinear regression showed that the BP neural network was a superior predictor of real-life situations. This study can be used to predict landslide slip of loess earthquakes. |
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| Keywords: | seismic loess landslide sliding distance BP neural network model sliding distance prediction |
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