Deformation prediction of the Northern Mountain landslide in Lijie Town of Zhouqu, Gansu Province based on long-short term memory network
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摘要:
立节镇北山滑坡长期处于蠕动变形状态，已多次发生滑坡、泥石流灾害。监测地表形变，以掌握灾害体地表形变规律，是实现地质灾害预警预报的可靠依据。文章引入一种机器学习模型——长短期记忆网络，通过立节北山监测点位移数据，运用该方法对立节北山滑坡变形进行预测，并且将预测结果与实际数据进行比对和分析。文章预测结果评价指标选用均方根误差、平均绝对误差、决定系数以及可解释方差，其中决定系数和可解释方差均达到0.99，预测值和真实值的拟合均方根误差和平均绝对误差也表现较低，说明长短期记忆网络在立节北山滑坡变形的预测中达到了良好的预测性能。
Abstract:The North Mountain landslide in Lijie Town has been in a long-term creeping deformation state and has experienced multiple landslide and debris flow disasters. Monitoring the surface deformation of landslide to grasp the surface deformation pattern of disaster body is a reliable basis for realizing early warning prediction of geological disaster. In this paper, a machine learning model is introduced to predict the relevant data, and a long and short-term memory network is used to predict the landslide deformation by monitoring the displacement data of North Mountain in Lijie, and the prediction results are compared with the actual data and analyzed. In this paper, root mean square error , mean absolute error , coefficient of determination and explainable variance are used to evaluate the prediction results, among which the coefficient of determination and explainable variance reach 0.99. It shows that the long short-term memory network used in this paper achieves good prediction performance in the prediction of landslide deformation in the North Mountain of Lijie.
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Key words:
- landslide /
- LSTM neural network /
- predictive analysis /
- North Mountain of Lijie /
- machine learning
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表 1 GNSS1垂直位移精度评价指标
Table 1. Evaluation metrics for vertical displacement precision of GNSS1
评价指标 RMSE/mm MAE/mm R2 Evar 数值 12.88 6.56 0.99 0.99 
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表 2 GNSS8垂直位移精度评价指标
Table 2. Evaluation metrics for vertical displacement precision of GNSS8
评价指标 RMSE/mm MAE/mm R2 Evar 数值 6.63 5.66 0.99 0.99
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表 3 GNSS8水平位移精度评价指标
Table 3. Evaluation metrics for horizontal displacement precision of GNSS8
评价指标 RMSE/mm MAE/mm R2 Evar 数值 4.00 3.79 0.99 0.99
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