Prediction of landslide dam stability and influencing factors analysis

被引:0
作者
Feng, Zhen-yu [1 ]
Zhou, Jia-wen [2 ]
Yang, Xing-guo [2 ]
Tan, Long-jin [1 ]
Liao, Hai-mei [1 ]
机构
[1] Guizhou Univ, Coll Civil Engn, Guiyang 550025, Peoples R China
[2] Sichuan Univ, State Key Lab Hydraul & Mt River Engn, Chengdu 610065, Peoples R China
基金
中国国家自然科学基金;
关键词
Landslide dam; Machine learning; Stability prediction; Model bias; Particle size distribution; ARTIFICIAL NEURAL-NETWORKS; INTERNAL STRUCTURE; FAILURE; EARTHQUAKE;
D O I
10.1016/j.enggeo.2025.108021
中图分类号
P5 [地质学];
学科分类号
0709 ; 081803 ;
摘要
Efficient prediction of landslide dam stability is crucial for emergency response and damage reduction. In this study, a comprehensive analysis is conducted on eight landslide dam characteristics. Four machine learning (ML) algorithms, namely Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Networks (ANN) and Logistic Regression (LR), are then applied to predict the stability of landslide dams. This prediction is based on two stability definitions: the dam's ability to endure for over a year and its collapse status at the time of the study. The results derived from the test set distinctly demonstrate that the RF model outperforms the other three ones in terms of its effectiveness. By employing the Synthetic Minority Over-sampling Technique (SMOTE), the issue of the RF model being biased towards predicting unstable dams due to imbalanced samples has been effectively alleviated. This approach resulted in overall accuracies of 76.19 % and 82.35 %, with biases of 0.8 % and 11.6 % and Classification Efficiency Index (CEI) values of 1.024 and 1.046, respectively, under the two stability definitions. Through Principal Component Analysis (PCA), it is further determined that the largest 5 % of particles constitute the primary materials influencing the stability of landslide dams. Additionally, a novel index termed the dam composition index (DCI) has been proposed to characterize the gradation of landslide dams. The proposed prediction method for landslide dam stability demonstrates outstanding performance and contributes to more effective emergency planning.
引用
收藏
页数:11
相关论文
共 60 条
[1]   Assessment of shallow landslide susceptibility by means of multivariate statistical techniques [J].
Baeza, C ;
Corominas, J .
EARTH SURFACE PROCESSES AND LANDFORMS, 2001, 26 (12) :1251-1263
[2]  
Boser B. E., 1992, Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, P144, DOI 10.1145/130385.130401
[3]   AN ANALYSIS OF TRANSFORMATIONS [J].
BOX, GEP ;
COX, DR .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1964, 26 (02) :211-252
[4]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[5]   Determining grain size distribution of the material composing landslide dams in the Northern Apennines: sampling and processing methods [J].
Casagli, N ;
Ermini, L ;
Rosati, G .
ENGINEERING GEOLOGY, 2003, 69 (1-2) :83-97
[6]  
Casagli N., 1999, Chikei, V20, P219
[7]   Back Analysis of the Draining Process of the Tangjiashan Barrier Lake [J].
Chen, Zuyu ;
Ma, Liqiu ;
Yu, Shu ;
Chen, Shujing ;
Zhou, Xingbo ;
Sun, Ping ;
Li, Xu .
JOURNAL OF HYDRAULIC ENGINEERING, 2015, 141 (04)
[8]  
Costa J.E., 1991, Open-File Report 1, P239
[9]  
COSTA JE, 1988, GEOL SOC AM BULL, V100, P1054, DOI 10.1130/0016-7606(1988)100<1054:TFAFON>2.3.CO
[10]  
2