Slope stability prediction using ensemble learning techniques: A case study in Yunyang County, Chongqing, China
Reads0
Chats0
TLDR
Wang et al. as discussed by the authors developed an ensemble learning-based method to predict the slope stability by introducing the random forest (RF) and extreme gradient boosting (XGBoost), which is applied to the stability prediction of 786 landslide cases in Yunyang County, Chongqing, China.Abstract:
Slope stability prediction plays a significant role in landslide disaster prevention and mitigation. This study develops an ensemble learning-based method to predict the slope stability by introducing the random forest (RF) and extreme gradient boosting (XGBoost). As an illustration, the proposed approach is applied to the stability prediction of 786 landslide cases in Yunyang County, Chongqing, China. For comparison, the predictive performance of RF, XGBoost, support vector machine (SVM), and logistic regression (LR) is systematically investigated based on the well-established confusion matrix, which contains the known indices of recall rate, precision, and accuracy. Furthermore, the feature importance of the 12 influencing variables is also explored. Results show that the accuracy of the XGBoost and RF for both the training and testing data is superior to that of SVM and LR, revealing the superiority of the ensemble learning models (i.e. XGBoost and RF) in the slope stability prediction of Yunyang County. Among the 12 influencing factors, the profile shape is the most important one. The proposed ensemble learning-based method offers a promising way to rationally capture the slope status. It can be extended to the prediction of slope stability of other landslide-prone areas of interest. read more
Citations
More filters
Journal ArticleDOI
Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors
Zhilu Chang,Filippo Catani,Fa-Qing Huang,Gengzhe Liu,Sansar Raj Meena,Jinsong Huang,Chuangbing Zhou +6 more
TL;DR: In this paper , the slope units extracted by the MSS method are used to construct LSP modeling, and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factor within slope unit using the descriptive statistics features of mean, standard deviation and range.
Journal ArticleDOI
Displacement prediction of Jiuxianping landslide using gated recurrent unit (GRU) networks
TL;DR: This study applies an advanced deep machine learning method called gated recurrent unit (GRU) to the displacement prediction of the Jiuxianping landslide, which is a typical reservoir landslide located in the Yunyang County of Chongqing, China.
Journal ArticleDOI
Displacement prediction of Jiuxianping landslide using gated recurrent unit (GRU) networks
Journal ArticleDOI
Closed-Form Equation for Estimating Unconfined Compressive Strength of Granite from Three Non-destructive Tests Using Soft Computing Models
Athanasia D. Skentou,Abidhan Bardhan,Anna Mamou,Minas E. Lemonis,G. Ganesh Kumar,Pijush Samui,Danial Jahed Armaghani,Panagiotis G. Asteris +7 more
TL;DR: In this paper , the use of three artificial neural network (ANN)-based models for the prediction of unconfined compressive strength (UCS) of granite using three non-destructive test indicators, namely pulse velocity, Schmidt hammer rebound number, and effective porosity, has been investigated.
Journal ArticleDOI
Multivariate adaptive regression splines analysis for 3D slope stability in anisotropic and heterogenous clay
TL;DR: In this paper , a study on the 3D undrained slopes in anisotropic and heterogenous clay using advanced upper and lower bounds finite element limit analysis (FELA) is presented, and the obtained stability solutions are normalized, and presented by a stability number that is a function of three geometrical ratios and two material ratios.
References
More filters
Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Proceedings ArticleDOI
XGBoost: A Scalable Tree Boosting System
Tianqi Chen,Carlos Guestrin +1 more
TL;DR: This paper proposes a novel sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning and provides insights on cache access patterns, data compression and sharding to build a scalable tree boosting system called XGBoost.
Journal ArticleDOI
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
TL;DR: It is demonstrated that a low variance is at least as important, as a non-negligible variance introduces the potential for over-fitting in model selection as well as in training the model, and some common performance evaluation practices are susceptible to a form of selection bias as a result of this form of over- fitting and hence are unreliable.
Journal ArticleDOI
A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring
TL;DR: A sequential ensemble credit scoring model based on a variant of gradient boosting machine (i.e., extreme gradient boosting (XGBoost) is proposed, which demonstrates that Bayesian hyper-parameter optimization performs better than random search, grid search, and manual search.
Journal ArticleDOI
Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization
TL;DR: Novel data-driven extreme gradient boosting (XGBoost) and random forest ensemble learning methods are applied for capturing the relationships between the USS and various basic soil parameters to predict undrained shear strength of soft clays.
Related Papers (5)
Multilayer hybrid ensemble machine learning model for analysis of Covid-19 vaccine sentiments
Vipin Jain,Kanchan Lata Kashyap +1 more