Model for estimating the percentage of deterioration of installed anti-personnelmines (APMs)
Introduction: This article presents research on the estimation of deterioration in deployed anti-personnel mines (APMs), developed in collaboration with the AICMA Group, with Argelia (Antioquia, Colombia) as a priority case study, and the Universidad Distrital in 2024 and 2025 years.
Problem: The widespread presence of APMs from the non-international armed conflict in Colombia poses a critical threat to communities and demining personnel. A key operational risk stems from the lack of accurate information regarding the physical degradation and functional status of buried devices, hindering safe and efficient humanitarian demining.
Objective: The study aims to develop a stochastic predictive model, based on artificial intelligence, to estimate the percentage of physical deterioration of installed APMs by integrating environmental variables, constituent materials, and exposure time.
Methodology: A neural network model was implemented in Python, processing key databases including mine records (AICMA), historical climatological data (IDEAM), material degradation coefficients, and soil erosion rates to capture non-linear degradation dynamics.
Results: The result is a model capable of estimating a quantifiable deterioration percentage for APMs. This output provides demining teams with a safety indicator to prioritize suspected hazardous areas with likely inactive devices and to select safer extraction methods, based on the estimated risk level.
Conclusion: The model represents a strategic technological advance for humanitarian demining in Colombia, offering an objective tool for operational decision-making that can optimize resource allocation, reduce personnel risks, and shorten clearance timelines in contaminated areas.
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