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21 February 2024 Landslide susceptibility assessment based on improved frequency ratio and XGBoost integrated model: a case study of Yingshan County
Weiliang Yang, Xiaoling Tan, Mulei Zhu, Siyuan Zhang
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Proceedings Volume 12988, Second International Conference on Environmental Remote Sensing and Geographic Information Technology (ERSGIT 2023); 129880Z (2024) https://doi.org/10.1117/12.3024221
Event: Second International Conference on Environmental Remote Sensing and Geographic Information Technology (ERSGIT 2023), 2023, Xi’an, China
Abstract
Obtaining accurate landslide susceptibility models for Yingshan County poses a challenge due to the presence of spatially heterogeneous landslide influencing factors (LIF) with significant uncertainties. Previous studies have utilized frequency ratio (FR) methods and XGBoost for landslide susceptibility mapping, but the results can still be improved. To address the limitations imposed by a single method, this paper proposes an improved frequency ratio (IFR) and XGBoost integrated model (IFR-XGBoost) and selects 12 LIFs for landslide susceptibility modeling. The results of accuracy, specificity, sensitivity, and ROC analysis indicate that all four models demonstrate accurate prediction of landslide susceptibility maps and the IFR-XGBoost model performs the best (AUC=0.93). These findings suggest that the IFR-XGBoost model proposed in this study is expected to produce accurate landslide susceptibility maps and provide guidelines for future scholars.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Weiliang Yang, Xiaoling Tan, Mulei Zhu, and Siyuan Zhang "Landslide susceptibility assessment based on improved frequency ratio and XGBoost integrated model: a case study of Yingshan County", Proc. SPIE 12988, Second International Conference on Environmental Remote Sensing and Geographic Information Technology (ERSGIT 2023), 129880Z (21 February 2024); https://doi.org/10.1117/12.3024221
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KEYWORDS
Data modeling
Integrated modeling
Machine learning
Statistical analysis
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