Prescribed Fire Smoke Monitoring: Failure-Case Simulation for Sensor Placement Decision-Making

Monitoring smoke from prescribed fires offers critical insights to stakeholders—from fire practitioners evaluating burn tactics to community leaders seeking to protect sensitive populations. Prescribed burns often occur in complex, coastal terrain, making it difficult to predict their smoke behavior. Whereas meteorological forecasts lack the resolution necessary to capture the terrain complexities, hyperlocal sensing networks can provide a toolset to understand an environment before, during and after a burn. The key factor limiting these sensing networks is their placement around burn perimeters. This study presents a methodology for optimizing the placement of smoke monitoring sensors around prescribed fire perimeters to maximize data capture. In collaboration with Stanford University's SMesh Project—a student-led initiative focused on deployable smoke sensor networks—we apply failure-case analysis to assess sensor network performance under varying operational scenarios. Since sensors are often deployed at least 24 hours before ignition, small deviations in forecasted wind conditions can drastically reduce monitoring effectiveness. Our approach integrates a terrain-aware Gaussian plume dispersion model with a fuzzing-based technique, introducing randomized perturbations to simulate wind uncertainty and evaluate plume behavior. Gaussian plume models are used as they are lighter-weight computationally than other approaches, particularly when modeling across hundreds or thousands of possible scenarios. To demonstrate our methods, we conducted simulations across three ecologically distinct sites in California—Malibu, Shasta, and Henry Coe State Park—to identify optimal placement strategies and failure modes. These findings support more resilient sensor network designs for prescribed fire operations. Upcoming field deployments during the 2025 Fall prescribed burn season will help validate and refine this approach.