A review of wildland fire spread modelling, 1990-present 3: Mathematical analogues and simulation models

In recent years, advances in computational power and spatial data analysis (GIS, remote sensing, etc) have led to an increase in attempts to model the spread and behvaiour of wildland fires across the landscape. This series of review papers endeavours to critically and comprehensively review all types of surface fire spread models developed since 1990. This paper reviews models of a simulation or mathematical analogue nature. Most simulation models are implementations of existing empirical or quasi-empirical models and their primary function is to convert these generally one dimensional models to two dimensions and then propagate a fire perimeter across a modelled landscape. Mathematical analogue models are those that are based on some mathematical conceit (rather than a physical representation of fire spread) that coincidentally simulates the spread of fire. Other papers in the series review models of an physical or quasi-physical nature and empirical or quasi-empirical nature. Many models are extensions or refinements of models developed before 1990. Where this is the case, these models are also discussed but much less comprehensively.Comment: 20 pages + 9 pages references + 1 page figures. Submitted to the International Journal of Wildland Fir

Distributed agent-based building evacuation simulator

The optimisation of the evacuation of a building plays a fundamental role in emergency situations. The behaviour of individuals, the directions that civilians receive, and the actions of the emergency personnel, will affect the success of the operation. We describe a simulation system that represents the individual, intelligent, and interacting agents that cooperate and compete while evacuating the building. The system also takes into account detailed information about the building and the sensory capabilities that it may contain. Since the level of detail represented in such a simulation can lead to computational needs that grow at least as a polynomial function of the number of the simulated agents, we propose an agent-oriented Distributed Building Evacuation Simulator (DBES). The DBES is integrated with a wireless sensor network which offers a closed loop representation of the evacuation procedure, including the sensed data and the emergency decision making

Modelling forest fire spread through discrete event simulation

First Online: 07 January 2024Forest fires are becoming a more common occurrence in Portugal as well as worldwide. To extinguish or reduce them more quickly and effectively, it is crucial to understand how they spread. This paper presents a study and a model that shows how wildfires spread, assuming the forest can be represented by a graph, where the nodes correspond to forest stands and the arcs to the path between them. In order to do this, algorithms were developed in Python, using discrete event simulation, that allow modelling the progression of the fire on the graph. This fire propagation model takes into account several aspects of the forest, the wind being the most influential one. Some tests were performed, considering different ignition points, wind directions and wind speeds.This work was supported by the Portuguese National Funding Agency for Science, Research and Technology (FCT), within the Center for Research and Development in Mathematics and Applications (CIDMA), project UIDB/04106/2020; and through project PCIF/GRF/0141/2019-An Optimization Framework to reduce Forest Fire

Cellular automata simulations of field scale flaming and smouldering wildfires in peatlands

In peatland wildfires, flaming vegetation can initiate a smouldering fire by igniting the peat underneath, thus, creating a positive feedback to climate change by releasing the carbon that cannot be reabsorbed by the ecosystem. Currently, there are very few models of peatland wildfires at the field-scale, hindering the development of effective mitigation strategies. This lack of models is mainly caused by the complexity of the phenomena, which involves 3-D spread and km-scale domains, and the very large computational resources required. This thesis aims to understand field-scale peatland wildfires, considering flaming and smouldering, via cellular automata, discrete models that use simple rules. Five multidimensional models were developed: two laboratory-scale models for smouldering, BARA and BARAPPY, and three field-scale models for flaming and smouldering, KAPAS, KAPAS II, and SUBALI. The models were validated against laboratory experiments and field data. BARA accurately simulates smouldering of peat with realistic moisture distributions and predicts the formation of unburned patches. BARAPPY brings physics into BARA and predicts the depth of burn profile, but needs 240 times more computational resources. KAPAS showed that the smouldering burnt area decreases exponentially with higher peat moisture content. KAPAS II integrates daily temporal variation of moisture content, and revealed that the omission of this temporal variation significantly underestimates the smouldering burnt area in the long term. SUBALI, the ultimate model of the thesis, integrates KAPAS II with BARA and considers the ground water table to predict the carbon emission of peatland wildfires. Applying SUBALI to Indonesia, it predicts that in El Niño years, 0.40 Gt-C in 2015 (literature said 0.23 to 0.51 Gt-C) and 0.16 Gt-C in 2019 were released, and 75% of the emission is from smouldering. This thesis provides knowledge and models to understand the spread of flaming and smouldering wildfires in peatlands, which can contribute to efforts to minimise the negative impacts of peatland wildfires on people and the environment, through faster-than-real-time simulations, to find the optimum firefighting strategy and to assess the vulnerability of peatland in the event of wildfires.Open Acces

Rumour Processes on N

We study four discrete time stochastic systems on \bbN modeling processes of rumour spreading. The involved individuals can either have an active or a passive role, speaking up or asking for the rumour. The appetite in spreading or hearing the rumour is represented by a set of random variables whose distributions may depend on the individuals. Our goal is to understand - based on those random variables distribution - whether the probability of having an infinite set of individuals knowing the rumour is positive or not