Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

Huang, Faming; Teng, Zuokui; Yao, Chi; Jiang, Shui-Hua; Catani, Filippo; Chen, Wei; Huang, Jinsong

doi:10.1016/j.jrmge.2023.11.001

Cited by 15 publications

(2 citation statements)

References 72 publications

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“…In addition, the study utilized AUC (area under the ROC curve) to quantitatively evaluate the performance of the machine learning methods in the study area (Huang et al, 2023). To verify the accuracy of the model, it was compared against survey point data of the study area.…”

Section: Accuracy Assessment Of the Modelsmentioning

confidence: 99%

Improving landslide susceptibility prediction through ensemble Recursive Feature Elimination and meta-learning framework

Halder,

Srivast,

Ghosh

et al. 2024

Preprint

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Landslides pose significant threats to local ecological environments, causing loss of life and economic damage. This research focuses on enhancing landslide susceptibility prediction in West Bengal's Sub-Himalayan region using an innovative ensemble Recursive Feature Elimination (RFE) and meta-learning framework. Seven advanced machine learning models- Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Extremely Randomized Trees (ExtraTrees), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Meta Classifier (MC) - were utilized alongside Remote Sensing and Geographic Information System techniques. Through feature selection, the ensemble identifies the most significant landslide conditioning factors. Evaluation metrics, including accuracy and AUC of the ROC curve, demonstrate the ensemble's superior predictive ability. Based on the findings, models perform well with LR (AUC = 0.935), SVM (AUC = 0.972), RF (AUC = 0.983), ExtraTrees (AUC = 0.985), GB (AUC = 0.987), and XGBoost (AUC = 0.987). However, the MC performed better than individual models with AUC = 0.987. The study's implications for land-use planning and disaster management in the region are discussed and by establishing a new benchmark for landslide susceptibility mapping, this research offers a promising approach for addressing similar environmental challenges worldwide, facilitating informed decision-making and mitigation efforts in geologically sensitive areas.

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Section: Accuracy Assessment Of the Modelsmentioning

confidence: 99%

Improving landslide susceptibility prediction through ensemble Recursive Feature Elimination and meta-learning framework

Halder,

Srivast,

Ghosh

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Because of natural geologic deposits and various error factors, soil properties have inherent spatial variability and randomness 10,11 . The objective existence of parameter uncertainty renders slope reliability analysis a prominent research area 12–15 . Stochastic anisotropy models of slopes and landslides are extensively built by considering the spatial variation of strength parameters, permeability coefficient, and Young's modulus 16–18 .…”

Section: Introductionmentioning

confidence: 99%

Slope stability analysis with a hypoplastic constitutive model: Investigating a stochastic anisotropy model and a hydro‐mechanical coupled simulation

Xue,

Miao,

Wang

et al. 2024

Num Anal Meth Geomechanics

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A reliable constitutive model is essential for accurately predicting slope deformation in numerical analysis, which includes assessing slope stability as an integral component. However, calculating slope stability in numerical models can be challenging due to complex boundary conditions and advanced constitutive models, especially when probabilistic and hydro‐mechanical coupled simulations are required. In this paper, we use a vector‐sum method‐based framework for slope stability analysis that incorporates a critical state hypoplastic constitutive model. The rationality and accuracy of the stability results are initially verified through critical slip surface analysis and a factor of safety evaluation. Subsequently, we investigate the probability‐based slope stability and failure characteristics by incorporating the spatial variability of strength parameters into the hypoplastic model. Finally, the Baishuihe landslide in the Three Gorges Reservoir Region is examined as a case study, where hydro‐mechanical coupled simulations are conducted under actual water level and rainfall conditions. The time‐varying stability and deformation characteristics of the landslide are analyzed and compared with the results obtained from the Mohr‐Coulomb model. The analysis results demonstrate the practicality and efficiency of using a hypoplastic model for probabilistic and time‐varying slope stability calculations. Furthermore, it highlights the superior ability of the hypoplastic model to accurately describe reservoir landslide deformation processes.

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The influence of cartographic representation on landslide susceptibility models: empirical evidence from a Brazilian UNESCO world heritage site

Araujo Junior,

Barella,

Eiras

et al. 2024

Nat Hazards

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Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

Cited by 15 publications

References 72 publications

Improving landslide susceptibility prediction through ensemble Recursive Feature Elimination and meta-learning framework

Improving landslide susceptibility prediction through ensemble Recursive Feature Elimination and meta-learning framework

Slope stability analysis with a hypoplastic constitutive model: Investigating a stochastic anisotropy model and a hydro‐mechanical coupled simulation

The influence of cartographic representation on landslide susceptibility models: empirical evidence from a Brazilian UNESCO world heritage site

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