Optimizing warning siren placement for audibility coverage using acoustic modelling and genetic algorithms

In contexts ranging from natural disasters to technological accidents and security threats, sirens play a crucial role in alerting the population by providing a rapid and widespread warning capability. Optimizing the spatial deployment of sirens to maximize audibility for the target population remains a critical and underexplored issue. In this study, we employ open-source tools for environmental noise modelling and multi-objective optimization: NoiseModelling, based on the CNOSSOS-EU propagation framework, and OpenMole, implementing the NSGA-II evolutionary algorithm. These tools are coupled to explore the solutions space and identify Pareto-optimal configurations according to two objectives: (1) the number of buildings exposed to sound levels above 80 dB, and (2) the total area exposed above this threshold. A case study on Saint Barthelemy Island suggests that, under the modelled conditions, the optimized Pareto front ranging from 7836 to 7858 dwellings and territorial coverage ranging from 15.29 to 15.31 [Formula: see text] yields higher predicted coverage than the configuration proposed by domain experts (6658 dwellings, 12.30 [Formula: see text]). The comparison between expert-based and model-based solutions reveals methodological limitations, such as the integration of non-acoustic contextual factors, and the strong potential of this approach as a decision-support framework for the design and evaluation of siren alert networks.