Multimodal Machine Learning to Predict Wild Boar Collision Risk on the French Railway Network

<jats:p> Wild boar ( <jats:italic>Sus scrofa</jats:italic> ) are the most prevalent species involved in wildlife-vehicle collision (WVC) incidents on the French railway network, resulting in direct mortality, operational delays and safety risks. More than 1,400 wild boar-related WVCs were recorded in 2025 by SNCF R&amp;eacute;seau, the national railway infrastructure operator. Strong recent increases are observed across all regions of metropolitan France, often following exponential growth. While railway ecology remains understudied compared to road-based WVC research, strong spatial clustering of observations and winter-dominated seasonal variation amplify the need for more targeted mitigation measures and data-driven decision-making. </jats:p> <jats:p> We therefore present a spatially and seasonally explicit, national-scale risk prioritization workflow for wild boar WVC projection on the French railway network. Our LightGBM machine learning regression models are based on a full decade of SNCF WVC records utilized as raw and smoothed target risk scores, as well as a comprehensive dataset of environmental covariates: At square kilometer resolution, we compile rail infrastructure and operation attributes, ecologically relevant land cover and habitat suitability maps, terrain and passability variables, as well as municipality-level hunting indices. Winter and summer risk are modelled as separate endpoints to account for seasonal shifts in animal behavior, hunting regime, and resource availability. Broad-scale 12 km smoothed models yield highest test-set R <jats:sup>2</jats:sup> values of 0.73 for summer and 0.81 for winter compared to other smoothing distances. By contrast, finer scale 5 km models retained higher predictive power for individual local hotspots. Spatial multiscale leave-out stress tests confirm generalization capability. </jats:p> <jats:p>We demonstrate a novel seasonal machine-learning method to derive risk surfaces and support railway biodiversity monitoring and wildlife risk management. The models provide a validated and transferable basis for producing risk maps, identifying ranked intervention hotspots, and projecting regional incident rates into the future.</jats:p>