389 1. INTRODUCTION Landslide susceptibility, defined as the propensity of an area to experience a specific landslide process, is a critical component of hazard assessment (INGEMMET, 2010). In Peru, landslides represent a persistent threat to infrastructure, communities, and mining operations due to steep Andean topography, active tectonics, and intense seasonal precipitation (Puente-Sotomayor et al., 2021). For the mining sector, slope instability directly affects haul roads, waste dumps, tailings storage facilities, access platforms, and associated infrastructure, potentially disrupting mineral production chains and increasing operational risk. Accurate susceptibility mapping is therefore essential not only for national planning but also for ensuring operational continuity and geotechnical risk management in mining environments. Landslide susceptibility zonation methods are commonly classified as qualitative (direct) and quantitative (indirect) approaches (Mandal & Mondal, 2019). In Peru, official susceptibility assessments have traditionally been developed at Macro-Regional (≥1:250,000) and Regional (≈1:100,000) scales following national geomorphological zoning standards. Macro-Regional maps provide broad territorial characterization based on lithological, structural, and morphoclimatic domains, whereas Regional maps refine susceptibility using expert-weighted thematic overlays of slope, geology, geomorphology, and land cover (Villacorta et al., 2012; Luque Poma et al., 2021). Although these maps provide comprehensive spatial coverage, their reliance on heuristic weighting schemes and fixed cartographic scales limits their quantitative transferability to engineering and mining applications, where higher spatial resolution and probabilistic consistency are required. Quantitative approaches attempt to reduce subjectivity by statistically relating landslide occurrences to conditioning factors. Bi-variate statistical models such as weights-of-evidence have been applied at local scale in northern Peru (Vilchez Mata & Medina Allcca, 2008), while recent machine learning frameworks have demonstrated improved predictive performance at regional scale (Kumar et al., 2023). However, no country-wide Artificial Neural Network (ANN) model has been systematically implemented and evaluated in Peru, nor has its behavior been assessed under spatial resolution changes relevant to mining-scale applications. This study addresses these gaps by developing a national-scale Landslide Susceptibility Map (LSM) for Peru using an Artificial Neural Network (ANN) framework trained on 105,181 samples derived from the INGEMMET landslide inventory. The model integrates DEM-derived morphometric variables (slope, aspect, curvature), hydrological indices (SPI, TWI), and land cover, and is implemented using resilient backpropagation. Beyond national hazard assessment, the proposed framework is designed to support operational decision-making in mining contexts by enabling quantitative identification of high-susceptibility zones within and around mining units. Such capability allows prioritization of stabilization measures, optimization of infrastructure layout, reduction of geotechnical risk, and minimization of production interruptions associated with mass movement events. The methodology further evaluates model behavior under spatial downscaling, supporting its applicability across multiple scales, from national planning to basin and minesite analysis. 2. MATERIALS & METHODS 2.1. Study Area The study area covers the Peruvian territory (≈1.285 million km²), extending between 0°02′– 18°21′34″ S and 68°39′07″–81°20′13″ W, and is dominated by the Central Andean Cordillera. Its relief results from long-term tectonic processes associated with Pacific–South American plate convergence,
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