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- W4385376143 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> Landslides represent one of the main geological hazards, especially in Chile. The main purpose of this study is to evaluate the application of machine learning algorithms (Support vector vachine, Random forest, XGBoost and logistic regression) and compare the results for the modeling of landslides susceptibility in the province of Chañaral, III region, Chile. A total of 86 sites are identified using various sources, plus another 86 sites as non-landslides, which are randomly divided, and then a cross-validation process is applied to calculate the accuracy of the models. After that, from 23 conditioning factors, 12 were chosen based on the information gain ratio (IGR). Subsequently, 5 factors are excluded by the correlation criterion, of which 2 that have not been used in the literature (Normalized difference glacier index, enhanced vegetation index) are used. The performance of the models is evaluated through the area under the ROC curve (AUC). To study the statistical behavior of the model, the Friedman non-parametric test is performed to compare the performance with the other algorithms and the Nemenyi test for pairwise comparison. Of the algorithms used, the RF (AUC = 0.9095) and the SVM (AUC = 0.9089) has the highest accuracy values measured in AUC compared to the other models and can be used for the same purpose in other geographic areas with similar characteristics. The findings of this investigation have the potential to assist in land use planning, landslide risk reduction, and informed decision making in the surrounding zones." @default.
- W4385376143 created "2023-07-30" @default.
- W4385376143 date "2023-07-30" @default.
- W4385376143 modified "2023-09-23" @default.
- W4385376143 title "Comment on nhess-2023-72" @default.
- W4385376143 doi "https://doi.org/10.5194/nhess-2023-72-cc1" @default.
- W4385376143 hasPublicationYear "2023" @default.
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