Refined and dynamic susceptibility assessment of landslides using InSAR and machine learning models

Geoscience Frontiers, Год журнала: 2024, Номер 15(6), С. 101890 - 101890

Опубликована: Июль 9, 2024

Landslide susceptibility assessment is crucial in predicting landslide occurrence and potential risks. However, traditional methods usually emphasize on larger regions of landsliding rely relatively static environmental conditions, which exposes the hysteresis refined-scale temporal dynamic changes. This study presents an improved approach by integrating machine learning models based random forest (RF), logical regression (LR), gradient boosting decision tree (GBDT) with interferometric synthetic aperture radar (InSAR) technology comparing them to their respective original models. The results demonstrated that combined improves prediction accuracy reduces false negative positive errors. LR-InSAR model showed best performance at both regional smaller scale, particularly when identifying areas high very susceptibility. Modeling were verified using data from field investigations including unmanned aerial vehicle (UAV) flights. great significance accurately assess help reduce prevent risk.

Язык: Английский

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Improving pixel-based regional landslide susceptibility mapping

Geoscience Frontiers, Год журнала: 2024, Номер 15(4), С. 101782 - 101782

Опубликована: Янв. 12, 2024

Regional landslide susceptibility mapping (LSM) is essential for risk mitigation. While deep learning algorithms are increasingly used in LSM, their extensive parameters and scarce labels (limited records) pose training challenges. In contrast, classical statistical algorithms, with typically fewer parameters, less likely to overfit, easier train, offer greater interpretability. Additionally, integrating physics-based data-driven approaches can potentially improve LSM. This paper makes several contributions enhance the practicality, interpretability, cross-regional generalization ability of regional LSM models: (1) Two new hybrid models, composed modules, proposed compared. Hybrid Model I combines infinite slope stability analysis (ISSA) logistic regression, a algorithm. II integrates ISSA convolutional neural network, representative techniques. The module constructs explanatory factor higher nonlinearity reduces prediction uncertainty caused by incomplete inventory pre-selecting non-landslide samples. captures relation between factors inventory. (2) A step-wise deletion process assess importance identify minimum necessary required maintain satisfactory model performance. (3) Single-pixel local-area samples compared understand effect pixel spatial neighborhood. (4) impact on performance explored. Typical landslide-prone regions Three Gorges Reservoir, China, as study area. results show that, testing region, using account neighborhoods, achieves roughly 4.2% increase AUC. Furthermore, models 30 m resolution land-cover data surpass those 1000 data, showing 5.5% improvement optimal set includes elevation, type, safety factor. These findings reveal key elements offering valuable insights practices.

Язык: Английский

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Uncertainties in landslide susceptibility prediction modeling: A review on the incompleteness of landslide inventory and its influence rules

Geoscience Frontiers, Год журнала: 2024, Номер 15(6), С. 101886 - 101886

Опубликована: Июль 1, 2024

Landslide inventory is an indispensable output variable of landslide susceptibility prediction (LSP) modelling. However, the influence incompleteness on LSP and transfer rules resulting error in model have not been explored. Adopting Xunwu County, China, as example, existing first obtained assumed to contain all samples under ideal conditions, after which different sample missing conditions are simulated by random sampling. It includes condition that whole study area randomly at proportions 10%, 20%, 30%, 40% 50%, well south County aggregation. Then, five machine learning models, namely, Random Forest (RF), Support Vector Machine (SVM), used perform LSP. Finally, results evaluated analyze uncertainties various conditions. In addition, this introduces interpretability methods explore changes decision basis RF Results show (1) certain (10%–50%) may affect for local areas. (2) Aggregation cause significant biases LSP, particularly areas where missing. (3) When 50% (either or aggregated), mainly manifested two aspects: first, importance ranking environmental factors slightly differs; second, regard modelling same test grid unit, weights individual drastically vary.

Язык: Английский

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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

Journal of Rock Mechanics and Geotechnical Engineering, Год журнала: 2023, Номер 16(1), С. 213 - 230

Опубликована: Ноя. 20, 2023

In the existing landslide susceptibility prediction (LSP) models, influences of random errors in conditioning factors on LSP are not considered, instead original directly taken as model inputs, which brings uncertainties to results. This study aims reveal influence rules different proportional uncertainties, and further explore a method can effectively reduce factors. The firstly used construct factors-based then 5%, 10%, 15% 20% added these for constructing relevant errors-based models. Secondly, low-pass filter-based models constructed by eliminating using filter method. Thirdly, Ruijin County China with 370 landslides 16 case. Three typical machine learning i.e. multilayer perceptron (MLP), support vector (SVM) forest (RF), selected Finally, discussed results show that: (1) decrease uncertainties. (2) With proportions increasing from 5% 20%, uncertainty increases continuously. (3) feasible absence more accurate (4) degrees two issues, errors, modeling large basically same. (5) Shapley values explain internal mechanism predicting susceptibility. conclusion, greater proportion higher uncertainty, errors.

Язык: Английский

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Autonomous prediction of rock deformation in fault zones of coal roadways using supervised machine learning

Tunnelling and Underground Space Technology, Год журнала: 2024, Номер 147, С. 105724 - 105724

Опубликована: Март 22, 2024

Язык: Английский

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A landslide susceptibility assessment method considering the similarity of geographic environments based on graph neural network

Gondwana Research, Год журнала: 2024, Номер 132, С. 323 - 342

Опубликована: Май 16, 2024

Язык: Английский

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Predicting Max Scour Depths near Two-Pier Groups Using Ensemble Machine-Learning Models and Visualizing Feature Importance with Partial Dependence Plots and SHAP

Journal of Computing in Civil Engineering, Год журнала: 2025, Номер 39(2)

Опубликована: Янв. 11, 2025

Язык: Английский

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Comprehensive review of remote sensing integration with deep learning in landslide forecasting and future directions

Natural Hazards, Год журнала: 2025, Номер unknown

Опубликована: Март 10, 2025

Язык: Английский

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Optimization method of conditioning factors selection and combination for landslide susceptibility prediction

Journal of Rock Mechanics and Geotechnical Engineering, Год журнала: 2024, Номер unknown

Опубликована: Авг. 1, 2024

Landslide susceptibility prediction (LSP) is significantly affected by the uncertainty issue of landslide related conditioning factor selection. However, most literature only performs comparative studies on a certain selection method rather than systematically study this issue. Targeted, aims to explore influence rules various commonly used methods LSP, and basis innovatively propose principle with universal application for optimal factors. An'yuan County in southern China taken as example considering 431 landslides 29 types Five methods, namely, correlation analysis (CA), linear regression (LR), principal component (PCA), rough set (RS) artificial neural network (ANN), are applied select combinations from original The results then inputs four common machine learning models construct 20 combined models, such CA-multilayer perceptron, CA-random forest. Additionally, multifactor-based multilayer perceptron random forest that selecting factors based proposed "accurate data, rich types, clear significance, feasible operation avoiding duplication" constructed comparisons. Finally, LSP uncertainties evaluated accuracy, index distribution, etc. Results show that: (1) have generally higher performance lower those selection-based models; (2) Influence degree different accuracy greater methods. Conclusively, above not ideal improving may complicate processes. In contrast, satisfied combination can be according principle.

Язык: Английский

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Landslide spatial prediction using cluster analysis

Gondwana Research, Год журнала: 2024, Номер 130, С. 291 - 307

Опубликована: Март 1, 2024

Язык: Английский

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