Landslide Susceptibility Mapping Using Machine Learning: A Case Study of Oregon

Abstract

Abstract Landslide susceptibility assessment, a crucial component of disaster prevention and mitigation, has traditionally relied on geological datasets and commonly used evaluation metrics. However, these metrics, often applied to 2D susceptibility maps, may not capture the full complexity of the issue. In this study, we incorporated spatial distribution data, including a historical landslide inventory, and integrated morphological, geological, hydrological, and land-use features. This approach enabled the construction of predictive models using eight distinct algorithms. The evaluation of classifier performance scores unveiled the superior performance of gradient tree boosting. In contrast, logistic regression struggled to handle the nonlinear aspects of landslide susceptibility analysis. A finding in our research was the consistent disparity in predictive susceptibility maps, even when similar performance scores were achieved across different algorithms. This underscores the necessity of combining evaluation metrics with spatial mapping in landslide susceptibility model assessment. We also highlighted the critical role of digital elevation models in constructing effective models when feature data was limited. Conversely, when abundant information was available, the integration of multi-source data significantly enhanced the precision of landslide susceptibility mapping. Overall, this study provides valuable insights for spatial prioritization in landslide field investigations and enhances our understanding of landslide risk assessment.