A Comparative Analysis of Decision Tree Models in Identifying Landslide Susceptibility and Type Classification
(2023) In Student thesis series INES NGEK01 20231Dept of Physical Geography and Ecosystem Science
- Abstract
- Landslides pose a significant risk to human life and infrastructure, especially in Italy, which has a high frequency of landslide occurrences. To mitigate these hazards, Landslide Susceptibility Mapping (LSM) is crucial for identifying risk areas and developing appropriate mitigation strategies. Various methodologies have been adapted to perform LSM, with machine learning models seeing a rise in popularity due their predictive capabilities. The aim of this study is to compare two ensemble tree models, Random Forest (RF) and Extreme Gradient Boosting (XGB), in their predictive performances for landslide susceptibility by type. The typical methodology for assessing landslide type is performing susceptibility assessments individually for... (More)
- Landslides pose a significant risk to human life and infrastructure, especially in Italy, which has a high frequency of landslide occurrences. To mitigate these hazards, Landslide Susceptibility Mapping (LSM) is crucial for identifying risk areas and developing appropriate mitigation strategies. Various methodologies have been adapted to perform LSM, with machine learning models seeing a rise in popularity due their predictive capabilities. The aim of this study is to compare two ensemble tree models, Random Forest (RF) and Extreme Gradient Boosting (XGB), in their predictive performances for landslide susceptibility by type. The typical methodology for assessing landslide type is performing susceptibility assessments individually for every class and aggregating the results. But with the RF and XGB models this process can be simplified by performing one multiclass analysis. The study found that the RF model significantly outperformed the XGB model in multiclass classification, with an overall accuracy of 95.83% compared to the 74.71% of the XGB model. No significant difference was found in the binary classification, with both models having an overall accuracy over 92%. The variables considered most important by both models were found to differ from heuristic models, suggesting a potential bias or incompleteness of the landslide inventory which should be considered in future studies. In conclusion, the RF model demonstrated its proficiency at making maps and high accuracy predictions for each landslide type. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9129786
- author
- Zuiverloon, Levi Jan LU
- supervisor
- organization
- course
- NGEK01 20231
- year
- 2023
- type
- M2 - Bachelor Degree
- subject
- keywords
- Landslides, landslide susceptibility mapping, Random Forest, Extreme Gradient Boosting, machine learning models, multiclass classification, binary classification, risk assessment, mitigation strategies, Italy, Aosta Valley, infrastructure vulnerability, supervised learning algorithms
- publication/series
- Student thesis series INES
- report number
- 605
- language
- English
- id
- 9129786
- date added to LUP
- 2024-01-22 10:46:46
- date last changed
- 2024-01-22 10:46:46
@misc{9129786, abstract = {{Landslides pose a significant risk to human life and infrastructure, especially in Italy, which has a high frequency of landslide occurrences. To mitigate these hazards, Landslide Susceptibility Mapping (LSM) is crucial for identifying risk areas and developing appropriate mitigation strategies. Various methodologies have been adapted to perform LSM, with machine learning models seeing a rise in popularity due their predictive capabilities. The aim of this study is to compare two ensemble tree models, Random Forest (RF) and Extreme Gradient Boosting (XGB), in their predictive performances for landslide susceptibility by type. The typical methodology for assessing landslide type is performing susceptibility assessments individually for every class and aggregating the results. But with the RF and XGB models this process can be simplified by performing one multiclass analysis. The study found that the RF model significantly outperformed the XGB model in multiclass classification, with an overall accuracy of 95.83% compared to the 74.71% of the XGB model. No significant difference was found in the binary classification, with both models having an overall accuracy over 92%. The variables considered most important by both models were found to differ from heuristic models, suggesting a potential bias or incompleteness of the landslide inventory which should be considered in future studies. In conclusion, the RF model demonstrated its proficiency at making maps and high accuracy predictions for each landslide type.}}, author = {{Zuiverloon, Levi Jan}}, language = {{eng}}, note = {{Student Paper}}, series = {{Student thesis series INES}}, title = {{A Comparative Analysis of Decision Tree Models in Identifying Landslide Susceptibility and Type Classification}}, year = {{2023}}, }