A four‐dimensional agent‐based model: A case study of forest‐fire smoke propagation

Abstract

Dynamic geospatial complex systems are inherently four‐dimensional (4D) processes and there is a need for spatio‐temporal models that are capable of realistic representation for improved understanding and analysis. Such systems include changes of geological structures, dune formation, landslides, pollutant propagation, forest fires, and urban densification. However, these phenomena are frequently analyzed and represented with modeling approaches that consider only two spatial dimensions and time. Consequently, the main objectives of this study are to design and develop a modeling framework for 4D agent‐based modeling, and to implement the approach to the 4D case study for forest‐fire smoke propagation. The study area is central and southern British Columbia and the western parts of Alberta, Canada for forest fires that occurred in the summer season of 2017. The simulation results produced realistic spatial patterns of the smoke propagation dynamics.

Assessing the Similarities of 3d Simulation Model Outcomes

“…This research study uses an existing ABM to simulate the propagation of forest fire smoke (Smith and Dragićević 2019) based on a hypothetical fire. The ABM operates with two primary agent types -fire agents and smoke agents.…”

“…Therefore, the main objective of this study is to compare the similarity of 3D simulation modeling outcomes using 3D Accuracy and 3D Kappa, and then to theoretically extend the analysis towards possible spatial extension of Kappa to 3D and 4D metrics. A previously developed 4D agent-based model (ABM) for forest fire smoke propagation (Smith and Dragićević 2019) was used to generate the simulation outcomes used as a case study for comparison of 3D simulation outcomes using 3D Accuracy and 3D Kappa measures.…”

Map Comparison Methods for Three‐Dimensional Space and Time Voxel Data

“…Voxel automata have been used to simulate landslides (Segre and Deangeli 1995), bed forms of dunes (Narteau et al 2009), root growth (Mulia, Dupraz, and van Noordwijk 2010), procedural cave generation (Cui, Chow, and Zhang 2011), dendrite growth (Eshraghi, Felicelli, and Jelinek 2012), or air pollution propagation (Jjumba and Dragićević 2015). The VA approach has been also further extended to agent‐based models (ABM) that operate in 4D (Smith and Dragićević 2019). In this research study, a 3D map is considered to be a cuboid containing voxels that are defined with coordinates ( x,y,z ) and class value for each voxel that together represent various 3D features as part of the 3D map.…”

“…For example, Light Detection And Ranging (LiDAR) technologies permit geospatial data collection in 3D point clouds (Woodhouse et al 2011), Building Information Models (BIM) provide representations of built structures in 3D (Jung and Joo 2011), as well as the rise of geosimulation models that operate in 3D (Torrens 2014; Koziatek and Dragicevic 2017; Smith and Dragićević 2019) or 4D, as 3D changing with time (Aurbach et al 2018; Zielinski, Voller, and Sorensen 2018). Model outputs are generated in 3D or 4D from various applications including plant growth (Greene 1989), weather forecasting (Grell et al 2005), soil organic carbon stocks (Poggio and Gimona 2014), ballistic impacts (Bova, Ciarallo, and Hill 2016), bird migration (Aurbach et al 2018), airborne pollutants (Jjumba and Dragićević 2015), or forest‐fire smoke modeling (Smith and Dragićević 2019), to name a few. There is a necessity to develop methods that allow for comparison of such multidimensional data or model outputs to ensure abilities of map comparison in 3D or 4D.…”

An Integrated Decision Support System for Improving Wildfire Suppression Management

“…Several research teams have been developing approaches based on GIS technology to allow the support of interventions and the management of firefighting assets during wildfire events [6]. Others simulate scenarios, to produce realistic spatial patterns that may predict when or where a critical situation may occur with a certain probability, thus enabling timely allocation of firefighting assets to those locations that present high fire risks [7]. GIS technology brings additional power to a fire-fighting response, whereby dangers and risks are evaluated, the necessary service demands are analyzed, and necessary resources are deployed.…”