A hybrid model to overcome landslide inventory incompleteness issue for landslide susceptibility prediction

Abstract

Landslide inventory incompleteness (LII) may significantly affect the model performance in landslide susceptibility mapping (LSM). However, traditional methods, including heuristic, statistical and deterministic models, cannot address LII issue. In this work, we introduce a novel hybrid LEO-MAHP model, blending landslide frequency, empirical adjustments, optimization functions, and multi-participated analytic hierarchy process to address it by taking Badong County as the study area. This hybrid model mitigates the drawbacks of data-heavy statistical approaches and subjective heuristic models by incorporating LII into weight determination. The findings show that the LEO-MAHP model demonstrates superior performance (AUROC = 0.809 and 0.805) over conventional statistical (AUROC = 0.714 and 0.770) and heuristic models (AUROC = 0.738 and 0.741) across different LII levels. We further discuss alternative LII solutions, proposing an updated landslide management strategy that accounts for climate change and human activities. Our findings underscore the necessity of evaluating LII before applying statistical or machine learning methods in LSM.

Keywords:

• Hybrid model
• landslide susceptibility
• optimization function
• prevention and control
• incomplete landslide inventory

Author contribution

All authors discussed the results and commented on the manuscript. Jiayao Tan contributed to conceptualization, data visualization and original draft preparation. Chi Yang contributed to conceptualization, methodology and formal analysis. Yuzhou Wang contributed to data visualization, original draft preparation, formal analysis, writing­review & editing. Hanxiang Xiong and Chuanming Ma were responsible for writing­review & editing, data curation, resources, investigation, methodology and supervision.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability statement

The authors do not have permission to share data.

Additional information

Funding

This work was supported by the Fundamental Research Funds for the Central University, China University of Geosciences (Wuhan) [No. CUGCJ1822].