Slope stability prediction using ensemble learning techniques: A case study in Yunyang County, Chongqing, China
168
TL;DR: 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.
read more
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
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
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.
119
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.
108
Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey
Moziihrii Ado,Khwairakpam Amitab,Arnab Kumar Maji,Elżbieta Jasińska,Radomir Gono,Zbigniew Leonowicz,Michal Jasinski +6 more
TL;DR: Machine learning models used for landslide susceptibility mapping are surveyed to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance.
A Novel Method using Explainable Artificial Intelligence (XAI)-based Shapley Additive Explanations for Spatial Landslide Prediction using Time-Series SAR dataset
Husam Abdulrasool H. Al-Najjar,Biswajeet Pradhan,Ghassan Beydoun,Raju Sarkar,Hyuck-Jin Park,Adbullah Alamri +5 more
TL;DR: In this article , an explainable artificial intelligence (XAI) was used for landslide prediction using synthetic-aperture radar (SAR) time series data, NDVI (normalized difference vegetation index) time-series data and other geo-environmental factors such as DEM (digital elevation model) derivatives.
88
Efficient time-variant reliability analysis of Bazimen landslide in the Three Gorges Reservoir Area using XGBoost and LightGBM algorithms
TL;DR: Wang et al. as mentioned in this paper developed an efficient time-variant reliability analysis approach by integrating the advanced machine learning algorithms of extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM).
86
References
Random Forests
Leo Breiman
- 01 Oct 2001
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.
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.
•Proceedings Article
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Balaji Krishnapuram,Mohak Shah,Alexander J. Smola,Charu C. Aggarwal,Dou Shen,Rajeev Rastogi +5 more
- 13 Aug 2016
TL;DR: The 2016 ACM Conference on Knowledge Discovery and Data Mining (KDD'16) as mentioned in this paper has attracted a significant number of submissions from countries all over the world, in particular, the research track attracted 784 submissions and the applied data science track attracted 331 submissions.
4.5K
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.
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.
712