Cited by Studying Wildfire Fronts Using Advection–Diffusion

Forest Fires: Silvicultural Prevention and Mathematical Models for Predicting Fire Propagation in Southern Italy DOI

Pasquale A. Marziliano, Fabio Lombardi, Maria Francesca Cataldo

et al.

Fire, Journal Year: 2024, Volume and Issue: 7(8), P. 278 - 278

Published: Aug. 7, 2024

In the Mediterranean basin, coniferous reforestation mainly comprises forest stands highly susceptible to fires. When silvicultural treatments have not been performed for decades after plantation, these often exhibit high vertical and horizontal tree density, along with a significant occurrence of lying standing deadwood, thereby increasing fuel load. On average, pine forests are characterized by values above-ground biomass, ranging from 175 254 Mg ha−1 younger older ones, respectively. The theoretical heat energy produced per surface unit, in case total combustion is also high, varying 300 450 MJ depending on stage stand development. this study, we demonstrated importance interventions reducing pyrological potential reforested located southern Italy, giving attention water savings needed during extinction phases. detail, applied preliminary mathematical reaction-diffusion model aimed at predicting development was using data obtained through estimation terms unit (1 hectare) variation critical intensity. We verified that, when applied, they induce reduction between 17 21%, while extinguishing saved ranges 600 1000 ha−1. Moreover, implemented, probability transition fire crown can be reduced up 31%. most effective results risk mitigation thinning canopy density carried out phases stands.

Language: Английский

Citations

Reinforcement learning-based dynamic field exploration and reconstruction using multi-robot systems for environmental monitoring DOI

Thinh Lu,

Divyam Sobti,

Deepak Talwar

et al.

Frontiers in Robotics and AI, Journal Year: 2025, Volume and Issue: 12

Published: March 25, 2025

In the realm of real-time environmental monitoring and hazard detection, multi-robot systems present a promising solution for exploring mapping dynamic fields, particularly in scenarios where human intervention poses safety risks. This research introduces strategy path planning control group mobile sensing robots to efficiently explore reconstruct field consisting multiple non-overlapping diffusion sources. Our approach integrates reinforcement learning-based algorithm guide formation identifying sources, with clustering-based method destination selection once new source is detected, enhance coverage accelerate exploration unknown environments. Simulation results real-world laboratory experiments demonstrate effectiveness our reconstructing fields. study advances has practical implications rescue missions explorations.

Language: Английский

Citations

Impact of topography and combustion functions on fire front propagation in an advection-diffusion-reaction model for wildfires DOI