Figure 2 – LSM of Peru derived from the ANN model. Blue dots represent mapped landslide occurrences. 4. DISCUSSION 4.1 Comparison between Landslide Susceptibility Maps in Peru To compare the proposed quantitative ANN-based landslide susceptibility map (LSM) with existing qualitative Macro-Regional and Regional LSMs for Peru, an independent validation dataset (2021–2023) was used. This dataset was not involved in model training or testing and therefore provides an unbiased benchmark. Model performance was evaluated using a spatial accuracy ratio defined as the proportion of validation landslide events located within areas classified as high or very high susceptibility: = ( ℎ+ ℎ )×100% (1) where = + + + ℎ + ℎ , represents the total number of validation landslide points across all susceptibility classes, where , , , ℎ and ℎ denote the number of validation points located within the very low (dark green), low (light green), moderate (yellow), high (orange), and very high (red) susceptibility classes, respectively. Using the independent validation dataset, the Macro-regional and Regional models achieved accuracies of 37.24% and 48.37%, respectively, whereas the ANN model reached
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