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

Pierre Aumond (), Aman Arora (), Paul Chapron () and Matthieu Péroche ()
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Pierre Aumond: UMRAE - Unité Mixte de Recherche en Acoustique Environnementale - Université de Lyon - Cerema - Centre d'Etudes et d'Expertise sur les Risques, l'Environnement, la Mobilité et l'Aménagement - Université Gustave Eiffel
Aman Arora: UMRAE - Unité Mixte de Recherche en Acoustique Environnementale - Université de Lyon - Cerema - Centre d'Etudes et d'Expertise sur les Risques, l'Environnement, la Mobilité et l'Aménagement - Université Gustave Eiffel
Paul Chapron: LASTIG - Laboratoire en Sciences et Technologies de l'Information Géographique - EIVP - Ecole des Ingénieurs de la Ville de Paris - Université Gustave Eiffel - Géodata Paris - Géodata Paris - IGN - Institut National de l'Information Géographique et Forestière [IGN] - Université Gustave Eiffel
Matthieu Péroche: LAGAM - Laboratoire de Géographie et d'Aménagement de Montpellier - UMPV - Université de Montpellier Paul-Valéry

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Abstract: 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 multiobjective 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 km 2 yields higher predicted coverage than the configuration proposed by domain experts (6658 dwellings, 12.30 km 2 ). 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.

Keywords: location-allocation problem; multi-objective optimization; CNOSSOS-EU; environmental acoustics; Siren (search for similar items in EconPapers)
Date: 2026-05-01
Note: View the original document on HAL open archive server: https://hal.science/hal-05715608v1
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Published in Geomatics, Natural Hazards and Risk, 2026, 17 (1), ⟨10.1080/19475705.2026.2663131⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05715608

DOI: 10.1080/19475705.2026.2663131

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