Preprints
https://doi.org/10.5194/nhess-2023-72
https://doi.org/10.5194/nhess-2023-72
23 Jun 2023
 | 23 Jun 2023
Status: this discussion paper is a preprint. It has been under review for the journal Natural Hazards and Earth System Sciences (NHESS). The manuscript was not accepted for further review after discussion.

Modeling and evaluation of the susceptibility to landslide events using machine learning algorithms in the province of Chañaral, Atacama region, Chile

Francisco Parra, Jaime González, Max Chacón, and Mauricio Marín

Abstract. 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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Francisco Parra, Jaime González, Max Chacón, and Mauricio Marín

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2023-72', Anonymous Referee #1, 12 Jul 2023
    • AC1: 'Reply on RC1', Francisco Parra, 14 Jul 2023
  • RC2: 'Comment on nhess-2023-72', Anonymous Referee #2, 13 Jul 2023
    • AC2: 'Reply on RC2', Francisco Parra, 24 Jul 2023
  • RC3: 'Comment on nhess-2023-72', Anonymous Referee #1, 14 Jul 2023
    • AC3: 'Reply on RC3', Francisco Parra, 24 Jul 2023
  • RC4: 'Comment on nhess-2023-72', Anonymous Referee #3, 28 Jul 2023
    • AC4: 'Reply on RC4', Francisco Parra, 30 Jul 2023
  • CC1: 'Comment on nhess-2023-72', Albert Cabré, 30 Jul 2023
    • AC5: 'Reply on CC1', Francisco Parra, 01 Aug 2023
      • CC3: 'Reply on AC5', Albert Cabré, 02 Aug 2023
        • AC6: 'Reply on CC3', Francisco Parra, 04 Aug 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2023-72', Anonymous Referee #1, 12 Jul 2023
    • AC1: 'Reply on RC1', Francisco Parra, 14 Jul 2023
  • RC2: 'Comment on nhess-2023-72', Anonymous Referee #2, 13 Jul 2023
    • AC2: 'Reply on RC2', Francisco Parra, 24 Jul 2023
  • RC3: 'Comment on nhess-2023-72', Anonymous Referee #1, 14 Jul 2023
    • AC3: 'Reply on RC3', Francisco Parra, 24 Jul 2023
  • RC4: 'Comment on nhess-2023-72', Anonymous Referee #3, 28 Jul 2023
    • AC4: 'Reply on RC4', Francisco Parra, 30 Jul 2023
  • CC1: 'Comment on nhess-2023-72', Albert Cabré, 30 Jul 2023
    • AC5: 'Reply on CC1', Francisco Parra, 01 Aug 2023
      • CC3: 'Reply on AC5', Albert Cabré, 02 Aug 2023
        • AC6: 'Reply on CC3', Francisco Parra, 04 Aug 2023
Francisco Parra, Jaime González, Max Chacón, and Mauricio Marín
Francisco Parra, Jaime González, Max Chacón, and Mauricio Marín

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Latest update: 06 Oct 2024
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Short summary
The objective of this work is to evaluate the susceptibility of mass removal in the province of Chañaral, Chile through the comparison of Machine Learning algorithms and the choice of factors. The results indicate that the most accurate algorithm in the study area corresponds to RF. On the other hand, and from 23 conditioning factors, 7 are chosen, which maximize the accuracy of the model. The results of this study are useful for the planning of relevant institutions.
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