Framework for submodel improvement in wildfire modeling

Houssami, Mohamad El; Lamorlette, Aymeric; Morvan, Dominique; Hadden, Rory M.; Simeoni, Albert

doi:10.1016/j.combustflame.2017.09.038

Cited by 32 publications

(21 citation statements)

References 64 publications

(105 reference statements)

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“…These models provide evidence in support of the design and execution of community measures. At a minimum, three different elements of WUI fires are apparent: fire models (considering aspects of wildfire propagation such as models for fire spread, smoke transport, spotting fires due to firebrands, etc., pedestrian models (evacuation models for pedestrian movement and behaviors), and traffic models (evacuation model for traffic movement also considering different transportation means for evacuation) . To date, the development and use of modeling tools from different areas of disaster response have been performed mostly independently (ie, with only limited coupling between fire models, pedestrian and transportation models).…”

Section: Current Modeling Of Wildfire Evacuee Responsementioning

confidence: 99%

Modeling and mapping dynamic vulnerability to better assess WUI evacuation performance

Gwynne¹,

Ronchi

Bénichou

et al. 2019

Fire and Materials

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Summary Wildland‐urban interface (WUI) fire incidents are likely to become more severe and will affect more and more people. Given their scale and complexity, WUI incidents require a multidomain approach to assess their impact and the effectiveness of any mitigation efforts. The authors recently produced a specification for a simulation framework that quantifies evacuation performance during WUI incidents including inputs from three core domains: fire development, pedestrian performance and vehicular traffic [26]. This framework could produce new insights by simulating evolving conditions of WUI incidents based on developments and interactions between the core components. Thus, it aims to overcome known limitations of previous approaches (eg, static assessment, single domain approaches, or lack of projection), as well as to provide explanatory insights into the outcomes produced by the simulation. The proposed framework would also advance geo‐spatial mapping of WUI incidents. The concept of dynamic vulnerability, 0.25emtrue0.25embold-italicv̇, is at the core of the framework and is enabled by the integrated simulation framework and the emergent conditions predicted. This allows users to construct richer incident narratives from the perspective of specific locations or subpopulations, and also makes fewer simplifying assumptions regarding interactions between the three core domains.

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Section: Current Modeling Of Wildfire Evacuee Responsementioning

confidence: 99%

Modeling and mapping dynamic vulnerability to better assess WUI evacuation performance

Gwynne¹,

Ronchi

Bénichou

et al. 2019

Fire and Materials

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“…The mean number of sensor that can detect fire can be obtained using (11) and (12) and is given by as follows:…”

Section: E Fire Detection Probability In the Presence Of Windmentioning

confidence: 99%

“…To analyze the coverage of a fire event using WSN, it is crucial to understand how wildfire grows with time. Different models were studied in [12] to understand the dynamics of wildfire propagation. The two primary approaches to model forest fire propagation are raster-based approach and vector based approach [13].…”

mentioning

confidence: 99%

Modeling and Analysis of Wildfire Detection using Wireless Sensor Network with Poisson Deployment

Pandey

Gupta

2018

2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)

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In many forest fire incidences, late detection of the fire has lead to severe damages to the forest and human property requiring more resources to gain control over the fire. An early warning and immediate response system can be a promising solution to avoid such massive losses. This paper considers a network consisting of multiple wireless sensors randomly deployed throughout the forest for early prompt detection of fire. We present a framework to model fire propagation in a forest and analyze the performance of considered wireless sensor network in terms of fire detection probability. In particular, this paper models sensor deployment as a Poisson point process (PPP) and models the forest fire as a dynamic event which expands with time. We also present various insights to the system including required sensor density and impact of wind velocity on the detection performance. We show that larger wind velocity may not necessarily imply bad sensing performance or the requirement of a denser deployment.

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“…First, we propose that such experiments should be developed and conducted within a hierarchical validation framework [17,31,32] that provides model assessment across the full range of complexity contained within a process-based model. These assessments will likely range from comparisons of the full model with large-scale field experiments (e.g., [2]) to comparisons of sub models (e.g., convective and radiative heat transfer or drag) with highly controlled laboratory experiments (e.g., [33]). Second, they should seek to estimate both measurement and experimental uncertainty for all information required by the process-based model to simulate the experiment, including initial and boundary conditions, material properties, and the fire behavior or physical metrics of interest.…”

mentioning

confidence: 99%

Advancing the Science of Wildland Fire Dynamics Using Process-Based Models

Hoffman

Sieg

Linn

et al. 2018

Fire

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As scientists and managers seek to understand fire behavior in conditions that extend beyond the limits of our current empirical models and prior experiences, they will need new tools that foster a more mechanistic understanding of the processes driving fire dynamics and effects. Here we suggest that process-based models are powerful research tools that are useful for investigating a large number of emerging questions in wildland fire sciences. These models can play a particularly important role in advancing our understanding, in part, because they allow their users to evaluate the potential mechanisms and interactions driving fire dynamics and effects from a unique perspective not often available through experimentation alone. For example, process-based models can be used to conduct experiments that would be impossible, too risky, or costly to do in the physical world. They can also contribute to the discovery process by inspiring new experiments, informing measurement strategies, and assisting in the interpretation of physical observations. Ultimately, a synergistic approach where simulations are continuously compared to experimental data, and where experiments are guided by the simulations will profoundly impact the quality and rate of progress towards solving emerging problems in wildland fire sciences.

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Framework for submodel improvement in wildfire modeling

Abstract: International audienc

Cited by 32 publications

References 64 publications

Modeling and mapping dynamic vulnerability to better assess WUI evacuation performance

Modeling and mapping dynamic vulnerability to better assess WUI evacuation performance

Modeling and Analysis of Wildfire Detection using Wireless Sensor Network with Poisson Deployment

Advancing the Science of Wildland Fire Dynamics Using Process-Based Models

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