Novel Intelligent Approach for the Early Warning of Rainfall-Type Landslides Based on the BRB Model
Publication: International Journal of Geomechanics
Volume 22, Issue 10
Abstract
This work attempts to apply belief rule-based (BRB) model in information fusion method to landslides, to improve the accuracy and efficiency for early warning of landslides. Taking a typical rainfall-type landslide as the experimental area, the monitoring results find that the surface displacement is the most sensitive monitoring data. It is determined that the monitoring data of surface displacement change rate and rainfall intensity could be used as the input parameter of the BRB model. An initial BRB model is established by setting up the rule base for discriminating warning levels. The data from three monitoring points are collected for the optimization of the initial BRB model, and verification of the optimized BRB models. Results shows the optimized BRB model can accurately describe the nonlinear relationship between the selected monitoring data and the warning level, which provides an intelligent method for landslide prevention and has a strong application prospect.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This study has been partially funded by the Key Research and Development Projects of Zhejiang Province (No. 2019C03104).
Notation
The following symbols are used in this paper:
- Ak
- kth rule;
- D
- consequent vector;
- referential value of the ith antecedent attribute;
- j
- 1, 2, … M;
- k
- 1, 2, … L;
- L
- number of all rules in the BRB model;
- M
- number of consequent vectors;
- N
- number of antecedent attributes;
- Q
- adjustable parameter set of the BRB model;
- xi
- input vector;
- Z
- number of samples input into the BRB model;
- ɛ(Q)
- difference between and ;
- τj,k
- belief degree;
- φk
- weights of the kth rule;
- ωi
- attribute weights of the ith antecedent attributes; and
- ξ(Q)
- difference between G and .
References
Bayer, B., A. Simoni, D. Schmidt, and L. Bertello. 2017. “Using advanced InSAR techniques to monitor landslide deformations induced by tunneling in the Northern Apennines, Italy.” Eng. Geol. 226: 20–32. https://doi.org/10.1016/j.enggeo.2017.03.026.
Bondur, V. G., T. N. Chimitdorzhiev, A. V. Dmitriev, P. N. Dagurov, A. I. Zakharov, and L. N. Zakharova. 2020. “Application of radar polarimetry to monitor changes in backscattering mechanisms in landslide zones using the example of the collapse of the Bureya River Bank.” Izv. Atmos. Ocean. Phys. 56 (9): 916–926. https://doi.org/10.1134/S0001433820090054.
Casagli, N., W. Frodella, S. Morelli, V. Tofani, A. Ciampalini, E. Intrieri, F. Raspini, G. Rossi, L. Tanteri, and P. Lu. 2017. “Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning.” Geoenviron. Disasters 4 (1): 9. https://doi.org/10.1186/s40677-017-0073-1.
Chen, H., Z. Zeng, and H. Tang. 2015. “Landslide deformation prediction based on recurrent neural network.” Neural Process Lett. 41 (2): 169–178. https://doi.org/10.1007/s11063-013-9318-5.
Hoang, N. D., and D. Tien Bui. 2018. “Spatial prediction of rainfall-induced shallow landslides using gene expression programming integrated with GIS: A case study in Vietnam.” Nat. Hazard. 92 (3): 1871–1887. https://doi.org/10.1007/s11069-018-3286-z.
Huggel, C., N. Khabarov, M. Obersteiner, and J. M. Ramírez. 2010. “Implementation and integrated numerical modeling of a landslide early warning system: A pilot study in Colombia.” Nat. Hazard. 52 (2): 501–518. https://doi.org/10.1007/s11069-009-9393-0.
Jafarian, Y., and A. Lashgari. 2016. “Simplified procedure for coupled seismic sliding movement of slopes using displacement-based critical acceleration.” Int. J. Geomech. 16 (4): 04015101. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000578.
Li, G., Z. Zhou, C. Hu, L. Chang, Z. Zhou, and F. Zhao. 2017. “A new safety assessment model for complex system based on the conditional generalized minimum variance and the belief rule base.” Saf. Sci. 93: 108–120. https://doi.org/10.1016/j.ssci.2016.11.011.
Li, P. L., W. P. Tian, and J. C. Li. 2012. “The application of bp neural network in the research of the landslide stability.” Adv. Mater. Res. 594–597: 2290–2295. https://doi.org/10.4028/www.scientific.net/AMR.594-597.2290.
Li, Y., H. Meng, Y. Dong, and S. Hu. 2004. “Types and characteristics of geological disasters in China – Analysis based on the results of national geological disaster investigation in counties and cities.” [In Chinese.] China J. Geol. Disasters Prev. 2004 (02): 32–37. https://doi.org/10.16031/j.cnki.issn.1003-8035.2004.02.005.
Liu, J., H. Tang, L. Qi, A. Su, Q. Liu, and C. Zhong. 2018. “Multi-sensor fusion of data for monitoring of Huangtupo landslide in the three gorges reservoir (China).” Geomatics Nat. Hazards Risk 9 (1): 881–891. https://doi.org/10.1080/19475705.2018.1478892.
Ravikumar, K., and A. Rajivkannan. 2020. “An enhancement of location estimation and disaster event prediction using density based SPATIO-temporal clustering with GPS.” Multimedia Tools Appl. 79 (5–6): 3929–3941. https://doi.org/10.1007/s11042-019-7583-7.
Senthilkumar, V., S. S. Chandrasekaran, and V. B. Maji. 2018. “Rainfall-induced landslides: Case study of the Marappalam Landslide, Nilgiris District, Tamil Nadu, India.” Int. J. Geomech. 18 (9): 05018006. https://doi.org/110.1061/(ASCE)GM.1943-5622.0001218.
Tan, Q., P. Wang, J. Hu, P. Zhou, M. Bai, and J. Hu. 2020. “The application of multi-sensor target tracking and fusion technology to the comprehensive early warning information extraction of landslide multi-point monitoring data.” Measurement 166: 108044. https://doi.org/10.1016/j.measurement.2020.108044.
Tianditu. 2021. “National platform for common geospatial information services.” Accessed October 12, 2021. https://www.tianditu.gov.cn.
Wang, W., N. B. He, K. M. Yao, and J. W. Tong. 2021. “Improved Kalman filter and its application in initial alignment.” Optik 226: 165747. https://doi.org/10.1016/j.ijleo.2020.165747.
Xu, D. L., J. Liu, J. B. Yang, G. P. Liu, J. Wang, I. Jenkinson, and J. Ren. 2007. “Inference and learning methodology of belief-rule-based expert system for pipeline leak detection.” Expert Syst. Appl. 32 (1): 103–113. https://doi.org/10.1016/j.eswa.2005.11.015.
Xu, X., H. Xu, C. Wen, J. Li, P. Hou, and J. Zhang. 2018. “A belief rule-based evidence updating method for industrial alarm system design.” Control Eng. Pract. 81 (DEC.): 73–84. https://doi.org/10.1016/j.conengprac.2018.09.001.
Yang, J. B., J. Liu, J. Wang, H. S. Sii, and H. W. Wang. 2006. “Belief rule-base inference methodology using the evidential reasoning approach-RIMER.” IEEE Trans. Syst. Man Cybern. Part A: Syst. Humans 36 (2): 266–285. https://doi.org/10.1109/TSMCA.2005.851270.
Yin, K. L., Q. H. Jiang, and Y. Wang. 2002. “Numerical simulation on the movement process of Xintan landslide by DDA method.” [In Chinese.] Chin J. Rock Mech. Eng. 21 (7): 959–962. https://doi.org/10.2753/CSH0009-4633350347.
Zhou, Z. J., L. L. Chang, C. H. Hu, X. X. Han, and Z. G. Zhou. 2016. “A new BRB-ER-based model for assessing the lives of products using both failure data and expert knowledge.” IEEE Trans. Syst. Man Cybern. Part A Syst. Humans. 46 (11): 1529–1543. https://doi.org/10.1109/TSMC.2015.2504047.
Information & Authors
Information
Published In
International Journal of Geomechanics
Volume 22 • Issue 10 • October 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Aug 2, 2021
Accepted: Feb 12, 2022
Published online: Jul 21, 2022
Published in print: Oct 1, 2022
Discussion open until: Dec 21, 2022
ASCE Technical Topics:
- Climates
- Continuum mechanics
- Disaster preparedness
- Disaster risk management
- Disaster warning systems
- Displacement (mechanics)
- Engineering fundamentals
- Engineering mechanics
- Environmental engineering
- Geohazards
- Geotechnical engineering
- Hydrologic data
- Hydrologic engineering
- Hydrology
- Landslides
- Meteorology
- Model accuracy
- Models (by type)
- Optimization models
- Precipitation
- Rainfall
- Rainfall intensity
- Solid mechanics
- Structural mechanics
- Water and water resources
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.