Forest‐Fire Models

Preisler, Haiganoush K.; Ager, Alan A.

doi:10.1002/9781118445112.stat07705

Cited by 5 publications

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The analogy to geometrical optics arises whenever fronts propagate in normal direction with a locally determined speed that is independent from, for example, front curvature, and thus found its application in other fields such as the prediction of forest fire fronts [ 38 ]. However, the analogy with optical phenomena is limited.…”

Section: Discussionmentioning

confidence: 99%

The collective effect of finite-sized inhomogeneities on the spatial spread of populations in two dimensions

Möbius

Tesser

Alards

et al. 2021

J. R. Soc. Interface.

View full text Add to dashboard Cite

The dynamics of a population expanding into unoccupied habitat has been primarily studied for situations in which growth and dispersal parameters are uniform in space or vary in one dimension. Here, we study the influence of finite-sized individual inhomogeneities and their collective effect on front speed if randomly placed in a two-dimensional habitat. We use an individual-based model to investigate the front dynamics for a region in which dispersal or growth of individuals is reduced to zero (obstacles) or increased above the background (hotspots), respectively. In a regime where front dynamics is determined by a local front speed only, a principle of least time can be employed to predict front speed and shape. The resulting analytical solutions motivate an event-based algorithm illustrating the effects of several obstacles or hotspots. We finally apply the principle of least time to large heterogeneous environments by solving the Eikonal equation numerically. Obstacles lead to a slow-down that is dominated by the number density and width of obstacles, but not by their precise shape. Hotspots result in a speed-up, which we characterize as function of hotspot strength and density. Our findings emphasize the importance of taking the dimensionality of the environment into account.

show abstract

Section: Discussionmentioning

confidence: 99%

The collective effect of finite-sized inhomogeneities on the spatial spread of populations in two dimensions

Möbius

Tesser

Alards

et al. 2021

J. R. Soc. Interface.

View full text Add to dashboard Cite

show abstract

“…The prediction of the wildfire propagation is a crucial functionality needed to enable any kind of proactive measure. For this reason, several models [22][23][24][25] have been proposed in the literature. By combining the model predictions with a real-time feedback gathered by distributed sensors and UAVs, it is possible to attain precise short-term forecasts on the fire evolution, further increasing the importance of an effective strategy for drone patrolling.…”

Section: Related Work and Main Contributionsmentioning

confidence: 99%

UAV Patrolling for Wildfire Monitoring by a Dynamic Voronoi Tessellation on Satellite Data

Giuseppi

Germana

Fiorini

et al. 2021

Drones

View full text Add to dashboard Cite

Fire monitoring and early detection are critical tasks in which Unmanned Aerial Vehicles (UAVs) are commonly employed. This paper presents a system to plan the drone patrolling schedule according to a real-time estimation of a fire propagation index that is derived from satellite data, such as the Normalized Difference Vegetation Index (NDVI) measurement and the Digital Elevation Model (DEM) of the surveilled area. The proposed system employs a waypoint scheduling logic, derived from a dynamic Voronoi Tessellation of the area, that combines characteristics of the territory (e.g., vegetation density) with real-time measurements (e.g., wind speed and direction). The system is validated on a case study in Italy, in the municipality of the city of L’Aquila, on three different fire scenarios. In normal situations, the designed waypoint-based navigation system provided an effective monitoring of the area, enabling the early detection of starting fires. The developed solution also demonstrated good performance in tracking and anticipating the fire front advance, potentially providing a better situational awareness to emergency operators and support their response policies. Both the test environment and the simulator have been made open-source.

show abstract

“…Several techniques and models have been proposed for modeling the spread of forest fires and fires [19], [20], [21], [22].Since our goal is to confirm the spread of fire per unit time, we constructed a simulated environment in a forest fire diffusion model based on cellular automata (CA) for collecting forest fire pattern data. The current CA model is often used as the base simulation model [23], [24], [25].Additionally, a CA-based simulation model for firefighting similar to the simulation environment of the proposed system has been studied [26], [27], [28], [29].CA also called a cell autologue, is a discrete dynamic system devised by American mathematicians Stanislaw Ulam and John von Neumann in the 1940s that has been studied in computational theory, mathematics, physics, complex systems, mathematical biology, and micro structural models.…”

Section: Cellular Automata Modelmentioning

confidence: 99%

Improvement of False Report Detection Performance Based on Invalid Data Detection Using Neural Network in WSNF

Lim¹,

Cho²

2018

IJCNC

View full text Add to dashboard Cite

WSN consists of a number of nodes and base stations and is used for event monitoring in various fields such as war situations, forest fires, and home networks. WSN sensor nodes are placed in fields that are difficult for users to manage. It is therefore vulnerable to attackers, and attackers can use false nodes or MAC injection attacks through the hijacked nodes to reduce the lifetime of the network or trigger false alarms. In order to prevent such attacks, several security protocols have been proposed, and all of them have been subjected to MAC-dependent validation, making it impossible to defend against false report attacks in extreme attack circumstances. As attacks have recently become more diverse and more intelligent, WSNs require intelligent methods of security. Based on the report information gathered from the base station, the proposed method provides a technique to prevent attacks that may occur where all MAC information is damaged by carrying out verification of a false report attack through the machine learning based prediction model and the evaluation function.

show abstract

Forest‐Fire Models

Cited by 5 publications

References 39 publications

The collective effect of finite-sized inhomogeneities on the spatial spread of populations in two dimensions

The collective effect of finite-sized inhomogeneities on the spatial spread of populations in two dimensions

UAV Patrolling for Wildfire Monitoring by a Dynamic Voronoi Tessellation on Satellite Data

Improvement of False Report Detection Performance Based on Invalid Data Detection Using Neural Network in WSNF

Contact Info

Product

Resources

About