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

doi:10.3799/dqkx.2023.003

Volume 49 Issue 5

May 2024

Turn off MathJax

Article Contents

Article Navigation > Earth Science > 2024 > 49(5): 1692-1705

Chen Mingxi, Tao Peijie, Zhou Chuangbing, Jiang Qinghui, 2024. Model Modification of Verhulst Inverse-Function Forecasting Model and Probabilistic Forecast for Landslide Failure Time. Earth Science, 49(5): 1692-1705. doi: 10.3799/dqkx.2023.003

Citation:

Chen Mingxi, Tao Peijie, Zhou Chuangbing, Jiang Qinghui, 2024. Model Modification of Verhulst Inverse-Function Forecasting Model and Probabilistic Forecast for Landslide Failure Time. Earth Science, 49(5): 1692-1705. doi: 10.3799/dqkx.2023.003

Citation:

Chen Mingxi, Tao Peijie, Zhou Chuangbing, Jiang Qinghui, 2024. Model Modification of Verhulst Inverse-Function Forecasting Model and Probabilistic Forecast for Landslide Failure Time. Earth Science, 49(5): 1692-1705. doi: 10.3799/dqkx.2023.003

PDF( 2437 KB)

Model Modification of Verhulst Inverse-Function Forecasting Model and Probabilistic Forecast for Landslide Failure Time

doi: 10.3799/dqkx.2023.003

1.
School of Civil Engineering and Architecture, Guangxi University, Nanning 530004, China
2.
School of Civil Engineering, Wuhan University, Wuhan 430072, China

Received Date: 2022-11-05
Available Online: 2024-06-04
Publish Date: 2024-05-25

Abstract

Abstract

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.
- landslides,
- landslide time-of-failure forecast,
- Verhulst inverse-function model,
- model modification,
- probabilistic analysis,
- forecasting decision,
- hazards

FullText(HTML)

References(59)

References

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). doi: 10.3969/j.issn.1007-3701.2018.04.002

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). doi: 10.3969/j.issn.1003-9805.2016.01.021

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 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). doi: 10.3321/j.issn:1000-6915.2009.06.003

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

陈贺, 汤华, 葛修润, 等, 2019. 基于深部位移的蠕滑型滑坡预警指标及预警预报研究. 岩石力学与工程学报, 38(S1): 3015-3024. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX2019S1042.htm

方汕澳, 许强, 修德皓, 等, 2021. 基于斜率模型的突发型黄土滑坡失稳时间预测. 水文地质工程地质, 48(4): 169-179. https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG202104022.htm

郭子正, 杨玉飞, 何俊, 等, 2022. 考虑注意力机制的新型深度学习模型预测滑坡位移. 地球科学, 1-21.

何满潮, 韩雪, 张斌, 等, 2012. 滑坡地质灾害远程实时监测预报技术与工程应用. 黑龙江科技学院学报, 22(4): 337-342. https://www.cnki.com.cn/Article/CJFDTOTAL-HLJI201204005.htm

贺小黑, 2018. Verhulst反函数滑坡预测预报模型的改进. 华南地质与矿产, 34(4): 289-293. https://www.cnki.com.cn/Article/CJFDTOTAL-HNKC201804004.htm

冷超勤, 张扬, 2016. 基于变形监测的金龙沟料场边坡失稳时间预测. 水电站设计, 32(1): 84-88. https://www.cnki.com.cn/Article/CJFDTOTAL-SDSJ201601021.htm

李天斌, 陈明东, 1996. 滑坡时间预报的费尔哈斯反函数模型法. 地质灾害与环境保护, 7(3): 13-17, 29. https://www.cnki.com.cn/Article/CJFDTOTAL-DZHB603.002.htm

龙万学, 林剑, 许湘华, 等, 2008. Verhulst反函数模型滑坡起始预测时刻的选择. 岩石力学与工程学报, 27(S1): 3298-3304. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX2008S1109.htm

亓星, 朱星, 许强, 等, 2020. 基于斋藤模型的滑坡临滑时间预报方法改进及应用. 工程地质学报, 28(4): 832-839. https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ202004017.htm

任开瑀, 姚鑫, 赵小铭, 等, 2020. 基于时序InSAR、GPS、影像偏移测量3种监测数据的滑坡失稳破坏预测研究. 岩石力学与工程学报, 39(S2): 3421-3431. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX2020S2021.htm

唐辉明, 李长冬, 胡伟, 等, 2022. 重大滑坡启滑的物理机制是什么?. 地球科学, 47(10): 3902-3903. doi: 10.3799/dqkx.2022.857

王东, 杜涵, 王前领, 2020. 基于系统聚类‒加权马尔科夫耦合模型滑坡预警方法研究与应用. 煤炭学报, 45(5): 1783-1794. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB202005024.htm

王智伟, 王利, 韩清清, 等, 2020. 一种位移传感器数据解码方法及其在滑坡监测中的应用. 大地测量与地球动力学, 40(4): 436-440. https://www.cnki.com.cn/Article/CJFDTOTAL-DKXB202004022.htm

许强, 2020. 对滑坡监测预警相关问题的认识与思考. 工程地质学报, 28(2): 360-374. https://www.cnki.com.cn/Article/CJFDTOTAL-GCDZ202002017.htm

许强, 汤明高, 黄润秋, 等, 2015. 大型滑坡监测预警与应急处置. 北京: 科学出版社.

许强, 曾裕平, 2009. 具有蠕变特点滑坡的加速度变化特征及临滑预警指标研究. 岩石力学与工程学报, 28(6): 1099-1106. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX200906005.htm

闫斌, 2021. 基于逆速率法的MT-InSAR高位滑坡监测及预警. 成都: 西南交通大学.

晏同珍, 1988. 滑坡动态规律及预测应用. 成都: 全国第三次工程地质大会论文选集(下卷), 37-43. https://cpfd.cnki.com.cn/Article/CPFDTOTAL-GCDZ198812002007.htm

喻小, 赵其华, 张埕豪, 等, 2019. GNSS实时监测在滑坡预警中的应用: 以陕西省周至G108路段滑坡为例. 人民长江, 50(10): 126-130, 142. https://www.cnki.com.cn/Article/CJFDTOTAL-RIVE201910022.htm

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(14) / Tables(3)

Get Citation

PDF

XML

Article views (522) PDF downloads(58)

Model Modification of Verhulst Inverse-Function Forecasting Model and Probabilistic Forecast for Landslide Failure Time

doi: 10.3799/dqkx.2023.003

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Proportional views

Export File

Citation

Format

Content