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.
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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 .
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© 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
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