PDF(3072 KB)
Verhulst反函数预测模型的改进及滑坡时间概率预测
陈铭熙, 陶培捷, 周创兵, 姜清辉
PDF(3072 KB)
PDF(3072 KB)
Verhulst反函数预测模型的改进及滑坡时间概率预测
Model Modification of Verhulst Inverse-Function Forecasting Model and Probabilistic Forecast for Landslide Failure Time
,
滑坡时间预测是滑坡灾害防治的重要组成部分，但由于滑坡演化存在不确定性，准确预测滑坡的发生极为困难.Verhulst反函数模型是一种常用的滑坡时间预测模型，但其存在计算起始时刻选择不当会造成监测数据拟合质量差和预测精度低的问题.针对这一不足，提出了一种改进的Verhulst反函数模型（MVIF模型），并进行近实时概率预测分析.结果表明：（1）MVIF模型改善了原模型对计算起始时刻选择苛刻的问题；（2）MVIF模型预测精度较高，在滑坡进入中等加速变形阶段之后可进行可靠的预测；（3）预测滑坡时间与破坏概率结合提供了一种新的滑坡预报准则.该研究可为蠕滑型滑坡的预警预报提供有价值的参考.
Landslide time-of-failure forecast is an essential part of landslide disaster prevention and control. However, due to the uncertainty of the landslide evolution process, it is challenging to forecast the occurrence time of landslide events accurately. The Verhulst inverse-function model is a common landslide time-of-failure forecasting model, but the model suffers from the problems of poor fitting quality and low forecasting accuracy of displacement monitoring data caused by the improper selection of the calculation starting points. To address this deficiency, an improved Verhulst inverse-function model (MVIF model) is proposed and analyzed for near real-time probabilistic forecast.The results show that (1) the MVIF model addresses the problem of harsh selection of the calculation starting points in the original model; (2) the MVIF model has high forecasting accuracy and can make reliable forecasts after the landslide enters the medium accelerating deformation phase;(3) the combination of predicted landslide time and failure probability provides a new forecasting criterion. This study can provide valuable reference for early warning and forecast of creeping landslides.
滑坡 / 滑坡时间预测 / Verhulst反函数模型 / 模型改进 / 概率分析 / 预测决策 / 灾害
landslides / landslide time-of-failure forecast / Verhulst inverse-function model / model modification / probabilistic analysis / forecasting decision / hazards
P694
|
Amitrano, D., Grasso, J. R., Senfaute, G., 2005. Seismic Precursory Patterns before a Cliff Collapse and Critical Point Phenomena. Geophysical Research Letters, 32(8): L08314. https://doi.org/10.1029/2004gl022270
|
|
Carey, J.M., Moore, R., Petley, D., et al., 2007. Pre- Failure Behaviour of Slope Materials and Their Significance in the Progressive Failure of Landslides. Landslides and Climate Change: Challenges and Solutions, London, 207-215.
|
|
Carlà, T., Farina, P., Intrieri, E., et al., 2017a. On the Monitoring and Early-Warning of Brittle Slope Failures in Hard Rock Masses: Examples from an Open-Pit Mine. Engineering Geology, 228: 71-81. https://doi.org/10.1016/j.enggeo.2017.08.007
|
|
Carlà, T., Intrieri, E., Di Traglia, F., et al., 2017b. Guidelines on the Use of Inverse Velocity Method as a Tool for Setting Alarm Thresholds and Forecasting Landslides and Structure Collapses. Landslides, 14(2): 517-534. https://doi.org/10.1007/s10346-016-0731-5
|
|
Carlà, T., Farina, P., Intrieri, E., et al., 2018. Integration of Ground-Based Radar and Satellite InSAR Data for the Analysis of an Unexpected Slope Failure in an Open-Pit Mine. Engineering Geology, 235: 39-52. https://doi.org/10.1016/j.enggeo.2018.01.021
|
|
Chen, H., Tang, H., Ge, X.R., et al., 2019. Study on Early Warning Index and Early Warning Forecast of Creep Landslide Based on Deep Displacement. Chinese Journal of Rock Mechanics and Engineering, 38(S1): 3015-3024 (in Chinese with English abstract).
|
|
Crosta, G. B., Agliardi, F., 2003. Failure Forecast for Large Rock Slides by Surface Displacement Measurements. Canadian Geotechnical Journal, 40(1): 176-191. https://doi.org/10.1139/t02-085
|
|
Fang, S.A., Xu, Q., Xiu, D.H., et al., 2021. A Study of the Predicted Instability Time of Sudden Loess Landslides Based on the SLO Model. Hydrogeology & Engineering Geology, 48(4): 169-179 (in Chinese with English abstract).
|
|
Fukuzono, T., 1985. A New Method for Predicting the Failure Time of a Slope. The Fourth International Conference and Field Workshop on Landslides, Tokyo, 145-150.
|
|
Gigli, G., Fanti, R., Canuti, P., et al., 2011. Integration of Advanced Monitoring and Numerical Modeling Techniques for the Complete Risk Scenario Analysis of Rockslides: The Case of Mt. Beni (Florence, Italy). Engineering Geology, 120(1/2/3/4): 48-59. https://doi.org/10.1016/j.enggeo.2011.03.017
|
|
Guo, Z.Z., Yang, Y.F., He, J., et al., 2022. A Novel Deep Learning Model to Predict Landslide Displacement Considering Attention Mechanism. Earth Science, 1-21 (in Chinese with English abstract).
|
|
Harries, N., Noon, D., Rowley, K., 2006. Case Studies of Slope Stability Radar Used in Open Cut Mines. In: Stability of Rock Slopes in Open Pit Mining and Civil Engineering Situations, Proceedings International Symposium on Stability of Rock Slopes in Open Pit Mining and Civil Engineering. The South African Institute of Mining and Metallurgy, Johannesburg, 335-342.
|
|
He, M.C., Han, X., Zhang, B., et al., 2012. Real-Time Remote Monitoring and Forecasting Technology for Landslide Disasters Based on Sliding Force Variation and Its Engineering Application. Journal of Heilongjiang Institute of Science and Technology, 22(4): 337-342 (in Chinese with English abstract).
|
|
He, X.H., 2018. Modification of Verhulst Inverse Function Model of Landslide Forecast. Geology and Mineral Resources of South China, 34(4): 289-293 (in Chinese with English abstract).
|
|
Hu, X. L., Wu, S. S., Zhang, G. C., et al., 2021. Landslide Displacement Prediction Using Kinematics-Based Random Forests Method: A Case Study in Jinping Reservoir Area, China. Engineering Geology, 283: 105975. https://doi.org/10.1016/j.enggeo.2020.105975
|
|
Intrieri, E., Carlà, T., Gigli, G., 2019. Forecasting the Time of Failure of Landslides at Slope-Scale: A Literature Review. Earth-Science Reviews, 193: 333-349. https://doi.org/10.1016/j.earscirev.2019.03.019
|
|
Kieffer, D. S., Valentin, G., Unterberger, K., 2016. Continuous Real-Time Slope Monitoring of the Ingelsberg in Bad Hofgastein, Austria. Geomechanics and Tunnelling, 9(1): 37-44. https://doi.org/10.1002/geot.201500047
|
|
Leng, C.Q., Zhang, Y., 2016. Prediction of Slope Instability Time in Jinlonggou Yard Based on Deformation Monitoring. Design of Hydroelectric Power Station, 32(1): 84-88 (in Chinese).
|
|
Li, T.B., Chen, M.D., 1996. Felhaas Inverse Function Model Method for Landslide Time Prediction. Journal of Geological Hazards and Environment Preservation, 7(3):13-17, 29 (in Chinese with English abstract).
|
|
Loew, S., Gschwind, S., Gischig, V., et al., 2017. Monitoring and Early Warning of the 2012 Preonzo Catastrophic Rockslope Failure. Landslides, 14(1): 141-154. https://doi.org/10.1007/s10346-016-0701-y
|
|
Long, W.X., Lin, J., Xu, X.H., et al., 2008. Selection of Initial Prediction Time of Landslide Based on Verhulst Inverse Function Model. Chinese Journal of Rock Mechanics and Engineering, 27(S1): 3298-3304 (in Chinese with English abstract).
|
|
Naismith, W.A., Wessels, S.D.N., 2005. Management of a Major Slope Failure at Nchanga Open Pit, Chingola, Zambia. Journal of the Southern African Institute of Mining and Metallurgy, 105(9):619-626. https://hdl.handle.net/10520/AJA0038223X_3086
|
|
Qi, X., Zhu, X., Xu, Q., et al., 2020. Improvement and Application of Landslide Proximity Time Prediction Method Based on Saito Model. Journal of Engineering Geology, 28(4): 832-839 (in Chinese with English abstract).
|
|
Ren, K.Y., Yao, X., Zhao, X.M., et al., 2020. Study on Landslide Instability and Failure Prediction Based on Time Series InSAR, GPS and Image Migration Measurement. Chinese Journal of Rock Mechanics and Engineering, 39(S2): 3421-3431 (in Chinese with English abstract).
|
|
Saito, M., 1965. Forecasting the Time of Occurrence of a Slope Failure. In: Proceedings of the 6th International Conference on Soil Mechanics and Foundation Engineering. Montreal, 2: 537-541.
|
|
Saito, M., 1979. Evidential Study on Forecasting Occurrence of Slope Failure. OYO Technical Report, 1:1-23.
|
|
Seguí, C., Rattez, H., Veveakis, M., 2020. On the Stability of Deep-Seated Landslides. The Cases of Vaiont (Italy) and Shuping (Three Gorges Dam, China). Journal of Geophysical Research: Earth Surface, 125(7): e2019JF005203. https://doi.org/10.1029/2019JF005203
|
|
Suwa, H., Mizuno, T., Ishii, T., 2010. Prediction of a Landslide and Analysis of Slide Motion with Reference to the 2004 Ohto Slide in Nara, Japan. Geomorphology, 124(3/4): 157-163. https://doi.org/10.1016/j.geomorph.2010.05.003
|
|
Tang, H.M., Li, C.D., Hu, W., et al., 2022. What is the Physical Mechanism of Major Landslides?. Earth Science, 47(10): 3902-3903 (in Chinese with English abstract).
|
|
Wang, D., Du, H., Wang, Q.L., 2020. Research and Application for Early Warning of Landslides Based on Hierarchical Clustering Coupling Weighting Markov Chain Model. Journal of China Coal Society, 45(5): 1783-1794 (in Chinese with English abstract).
|
|
Wang, H. H., Zhong, P., Xiu, D. H., et al., 2022. Monitoring Tilting Angle of the Slope Surface to Predict Loess Fall Landslide: An on-Site Evidence from Heifangtai Loess Fall Landslide in Gansu Province, China. Landslides, 19(3): 719-729. https://doi.org/10.1007/s10346-021-01727-0
|
|
Wang, Z.W., Wang, L., Han, Q.Q., et al., 2020. Data Decoding Method of the Displacement Sensor and Its Application in Landslide Monitoring. Journal of Geodesy and Geodynamics, 40(4): 436-440 (in Chinese with English abstract).
|
|
Xu, Q., 2020. Understanding the Landslide Monitoring and Early Warning: Consideration to Practical Issues. Journal of Engineering Geology, 28(2): 360-374 (in Chinese with English abstract).
|
|
Xu, Q., Tang, M.G., Huang, R.Q., et al., 2015. Monitoring, Early Warning, and Emergency Disposal of Large-Scale Landslides. Science Press, Beijing (in Chinese).
|
|
Xu, Q., Zeng, Y.P., 2009. Research on Acceleration Variation Characteristics of Creep Landslide and Early- Warning Prediction Indicator of Critical Sliding. Journal of Rock Mechanics and Engineering, 28(6): 1099-1106 (in Chinese with English abstract).
|
|
Yamaguchi, U., Shimotani, T., 1986. A Case Study of Slope Failure in a Limestone Quarry. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 23(1): 95-104. https://doi.org/10.1016/0148-9062(86)91670-0
|
|
Yan, B., 2021. MT-InSAR Technology Combined with Inverse-Velocity Method for High Level Landslide Monitoring and Early Warning. Southwest Jiaotong University, Chengdu (in Chinese with English abstract).
|
|
Yan, T.Z., 1998. Landslide Dynamic Law and Forecasting Application. The 3th National Congress of Engineering Geology, Chengdu, 37-43 (in Chinese).
|
|
Yu, X., Zhao, Q.H., Zhang, C.H., et al., 2019. Application of GNSS Real-Time Monitoring in Landslide Early Warning: Case of Landslide of G108 Section in Zhouzhi County, Shannxi Province. Yangtze River, 50(10): 126-130, 142 (in Chinese with English abstract).
|
|
Zhang, J., Wang, Z. P., Zhang, G. D., et al., 2020. Probabilistic Prediction of Slope Failure Time. Engineering Geology, 271: 105586. https://doi.org/10.1016/j.enggeo.2020.105586
|
/
| 〈 |
|
〉 |
AI Summary
