Comment on egusphere-2025-1169

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Abstract
Effective landslide prevention and mitigation necessitate the development of reliable landslide susceptibility maps. However, previous studies have primarily focused on assessing the overall performance of predicted susceptibility rather than examining the spatial characteristics of the predicted Landslide Susceptibility Index (LSI). This study aims to evaluate the efficacy of predicted LSIs derived from widely used statistical models while considering the spatial characteristics of landslides. To achieve this goal, four commonly used LSI models, namely logistic regression (LR), certainty factor (CF), frequency ratio (FR), and information value (IV), were utilized to map landslide susceptibility in Jecheon, South Korea. The models were developed using 112 landslide inventories and taking into account topography, hydrogeology, soils, forests, and lithological heterogeneities. Subsequently, the predicted LSIs were compared with the 1D topography profiles of recent debris events delineated from the high-resolution aerial and drone imagery. The distribution of anticipated LSIs along the landslide source area to the landslide runout and deposit zones was found to be inconsistent with the landslide characteristics. Nevertheless, the overall accuracy of the FR, IV, CF, and LR models demonstrated the strong predictive capabilities of these models. To address this spatial inconsistency issue, we proposed a hybrid integrated approach to achieve higher accuracy than the individual LSI models. Subsequently, a landslide hazard microzonation map was prepared and validated based on the in-situ observations and inventory data. It was observed that 94.6 % of landslide inventory occurrences fell within severe to high susceptibility zones. Precision results, such as an area under the curve of 0.906, mean square error of 0.25, mean absolute error of 0.08, root mean square error of 0.28, and a precision of 88.3 %, suggest that the hybrid integrated approach is more useful for landcover planning and mitigating landslide-induced disaster risks compare to individual LSI models.

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