Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 332, P. 117357 - 117357

Published: Feb. 1, 2023

Language: Английский

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Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost)

Arabian Journal for Science and Engineering, Journal Year: 2022, Volume and Issue: 47(6), P. 7367 - 7385

Published: Jan. 17, 2022

Language: Английский

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Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea

Journal of Environmental Management, Journal Year: 2021, Volume and Issue: 305, P. 114367 - 114367

Published: Dec. 27, 2021

Language: Английский

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Landslide Susceptibility mapping using random forest and extreme gradient boosting: A case study of Fengjie, Chongqing

Geological Journal, Journal Year: 2023, Volume and Issue: 58(6), P. 2372 - 2387

Published: Feb. 7, 2023

Landslide susceptibility analysis can provide theoretical support for landslide risk management. However, some analyses are not sufficiently interpretable. Moreover, the accuracy of many research methods needs to be improved. Therefore, this study supplement these deficiencies. This aims evaluation effects random forest (RF) and extreme gradient boosting (XGBoost) classifier models on susceptibility, compare their applicability in Fengjie County, Chongqing, a typical landslide‐prone area southwest China. Firstly, 1624 landslides information from 1980 2020 were obtained through field investigation, geospatial database 16 conditional factors had been constructed. Secondly, non‐landslide points selected form complete data set RF XGBoost established. Finally, under ROC curve (AUC) value, accuracy, F ‐score used two models. The results show that even though both classifiers have highly accurate model performs better. In comparison, has higher AUC value 0.866, its approximately 2% than XGBoost. land use, elevation, lithology County contribute occurrence landslides. is due human engineering activities (such as reclamation, housing construction) resulting low slope stability widely distributed sandstone, siltstone, mudstone layers owing permeability planes weakness.

Language: Английский

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Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey

Earth Science Informatics, Journal Year: 2023, Volume and Issue: 16(1), P. 397 - 414

Published: Jan. 31, 2023

Language: Английский

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Optimizing landslide susceptibility mapping using machine learning and geospatial techniques

Ecological Informatics, Journal Year: 2024, Volume and Issue: 81, P. 102583 - 102583

Published: March 30, 2024

Landslides present a substantial risk to human lives, the environment, and infrastructure. Consequently, it is crucial highlight regions prone future landslides by examining correlation between past various geo-environmental factors. This study aims investigate optimal data selection machine learning model, or ensemble technique, for evaluating vulnerability of areas determining most accurate approach. To attain our objectives, we considered two different scenarios selecting landslide-free random points (a slope threshold buffer-based approach) performed comparative analysis five models landslide susceptibility mapping, namely: Support Vector Machine (SVM), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Random Forest (RF), Extreme Gradient Boosting (XGBoost). The area this research an in Polk County Western North Carolina that has experienced fatal landslides, leading casualties significant damage infrastructure, properties, road networks. model construction process involves utilization dataset comprising 1215 historical occurrences non-landslide points. We integrated total fourteen geospatial layers, consisting topographic variables, soil data, geological land cover attributes. use metrics assess models' performance, including accuracy, F1-score, Kappa score, AUC-ROC. In addition, used seeded-cell index (SCAI) evaluate map consistency. using Weighted Average produces outstanding results, with AUC-ROC 99.4% scenario 91.8% scenario. Our findings emphasize impact sampling on performance mapping. Furthermore, optimally identifying landslide-prone hotspots need urgent management planning, demonstrates effectiveness analyzing providing valuable insights informed decision-making disaster reduction initiatives.

Language: Английский

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Impact assessment of geohazards triggered by 6 February 2023 Kahramanmaras Earthquakes (Mw 7.7 and Mw 7.6) on the natural gas pipelines

Engineering Geology, Journal Year: 2024, Volume and Issue: 334, P. 107508 - 107508

Published: April 16, 2024

Language: Английский

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Application of Bayesian Hyperparameter Optimized Random Forest and XGBoost Model for Landslide Susceptibility Mapping

Frontiers in Earth Science, Journal Year: 2021, Volume and Issue: 9

Published: July 16, 2021

Landslides are widely distributed worldwide and often result in tremendous casualties economic losses, especially the Loess Plateau of China. Taking Wuqi County hinterland as research area, using Bayesian hyperparameters to optimize random forest extreme gradient boosting decision trees model for landslide susceptibility mapping, two optimized models compared. In addition, 14 influencing factors selected, 734 landslides obtained according field investigation reports from literals. The were randomly divided into training data (70%) validation (30%). tree a algorithm, then optimal selected mapping. Both evaluated compared receiver operating characteristic curve confusion matrix. results show that AUC 0.88 0.86, respectively, which showed an improvement 4 3%, indicating prediction performance has been improved. However, higher predictive ability than model. Thus, hyperparameter optimization is great significance accuracy Therefore, can generate high-quality map.

Language: Английский

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Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China

Computers & Geosciences, Journal Year: 2021, Volume and Issue: 158, P. 104966 - 104966

Published: Oct. 27, 2021

Language: Английский

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Prediction of flyrock induced by mine blasting using a novel kernel-based extreme learning machine

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2021, Volume and Issue: 13(6), P. 1438 - 1451

Published: Sept. 20, 2021

Blasting is a common method of breaking rock in surface mines. Although the fragmentation with proper size main purpose, other undesirable effects such as flyrock are inevitable. This study carried out to evaluate capability novel kernel-based extreme learning machine algorithm, called kernel (KELM), by which distance (FRD) predicted. Furthermore, three data-driven models including local weighted linear regression (LWLR), response methodology (RSM) and boosted tree (BRT) also developed validate model. A database gathered from quarry sites Malaysia employed construct proposed using 73 sets spacing, burden, stemming length powder factor data inputs FRD target. Afterwards, validity evaluated comparing corresponding values some statistical metrics validation tools. Finally, results verify that KELM model on account highest correlation coefficient (R) lowest root mean square error (RMSE) more computationally efficient, leading better predictive compared LWLR, RSM BRT for all sets.

Language: Английский

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