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Fire
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Applications of Computational Statistics to Wildfire Science and Management
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Applications of Computational Statistics to Wildfire Science and Management

A special issue of Fire (ISSN 2571-6255).

Deadline for manuscript submissions: 31 March 2025 | Viewed by 4042

Special Issue Editor

Prof. Dr. W. John Braun

E-Mail Website
Guest Editor
Department of Computer Science, Mathematics, Physics and Statistics, University of British Columbia, Kelowna, BC, Canada
Interests: computational statistics; modeling and simulation with applications to wildfire science

Special Issue Information

Dear Colleagues,

Fire safety is of paramount importance, as it concerns life, property, and the environment. Computational statistics has emerged as a powerful tool in various fields, including wildfire science. Its applications range from understanding the fundamental processes of fire ignition and spread to developing fire prevention and suppression strategies.

This Special Issue aims to showcase the latest advancements in computational statistics and its applications in fire science. We cordially invite researchers from academia and industry to share their latest research findings, including various aspects of fire safety. Through the application of simulation, modeling, and data-driven methods, a deeper understanding of fire behavior, fire dynamics, and the effectiveness of fire safety measures can be achieved, thereby driving innovation in the field of fire safety.

We welcome original research articles and reviews that address the following themes:

  • Fire dynamics and simulation: this includes the development and application of computational models to predict the behavior of fires, such as ignition, growth, spread, fire-spotting, and suppression.
  • Fire risk assessment and management: this theme covers the use of statistical methods and models to assess fire risks and fuel treatment strategies, as well as prescribed and cultural burning.
  • Fire detection and alarm systems: this area includes the design and evaluation of fire detection and alarm systems based on data-driven approaches and machine learning techniques.
  • Fire protection engineering: this theme focuses on the application of computational statistics in the design and optimization of fire protection systems, such as sprinklers, smoke detectors, and fire doors.
  • Smoke transport and the health effects of smoke: this theme includes smoke monitoring techniques, as well as the epidemiological consequences of increased particulate matter in communities. 

I look forward to receiving your contributions.

Prof. Dr. W. John Braun
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fire is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fire safety
  • fire simulation
  • fire risk assessment
  • fire management
  • statistical applications

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

13 pages, 2119 KiB  
Article
Mapping Variable Wildfire Source Areas Through Inverse Modeling
by Stephen W. Taylor, Nicholas Walsworth and Kerry Anderson
Fire 2024, 7(12), 454; https://doi.org/10.3390/fire7120454 - 3 Dec 2024
Viewed by 457
Abstract
Global climate change is leading to increased wildfire activity in many parts of the world, and with increasing development, a heightened threat to communities in the wildland urban interface. Evaluating the potential for fire to affect communities and critical infrastructure is essential for [...] Read more.
Global climate change is leading to increased wildfire activity in many parts of the world, and with increasing development, a heightened threat to communities in the wildland urban interface. Evaluating the potential for fire to affect communities and critical infrastructure is essential for effective response decision-making and resource prioritization, including evacuation planning, with changing weather conditions during the fire season. Using a receptor–pathway–source assessment framework, we estimate the potential source area from which a wildfire could spread to a community in British Columbia by projecting fire growth outward from the community’s perimeter. The outer perimeter of the source area is effectively an evacuation trigger line for the forecast period. The novel aspects of our method are inverting fire growth in both space and time by reversing the wind direction, the time course of hourly weather, and slope and aspect inputs to a time-evolving fire growth simulation model Prometheus. We also ran a forward simulation from the perimeter of a large fire that was threatening the community to the community edge and back. In addition, we conducted a series of experiments to examine the influence of varying environmental conditions and ignition patterns on the invertibility of fire growth simulations. These cases demonstrate that time-evolving fire growth simulations can be inverted for practical purposes, although caution is needed when interpreting results in areas with extensive non-fuel cover or complex community perimeters. The advantages of this method over conventional simulation from a fire source are that it can be used for pre-attack planning before fire arrival, and following fire arrival, it does not require having an up-to-the-minute map of the fire location. The advantage over the use of minimum travel time methods for inverse modeling is that it allows for changing weather during the forecast period. This procedure provides a practical tool to inform real-time wildfire response decisions around communities, including resource allocation and evacuation planning, that could be implemented with several time-evolving fire growth models. Full article
(This article belongs to the Special Issue Applications of Computational Statistics to Wildfire Science and Management)
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13 pages, 2116 KiB  
Article
Dead Fuel Moisture Content Reanalysis Dataset for California (2000–2020)
by Angel Farguell, Jack Ryan Drucker, Jeffrey Mirocha, Philip Cameron-Smith and Adam Krzysztof Kochanski
Fire 2024, 7(10), 358; https://doi.org/10.3390/fire7100358 - 9 Oct 2024
Viewed by 739
Abstract
This study presents a novel reanalysis dataset of dead fuel moisture content (DFMC) across California from 2000 to 2020 at a 2 km resolution. Utilizing a data assimilation system that integrates a simplified time-lag fuel moisture model with 10-h fuel moisture observations from [...] Read more.
This study presents a novel reanalysis dataset of dead fuel moisture content (DFMC) across California from 2000 to 2020 at a 2 km resolution. Utilizing a data assimilation system that integrates a simplified time-lag fuel moisture model with 10-h fuel moisture observations from remote automated weather stations (RAWS) allowed predictions of 10-h fuel moisture content by our method with a mean absolute error of 0.03 g/g compared to the widely used Nelson model, with a mean absolute error prediction of 0.05 g/g. For context, the values of DFMC in California are commonly between 0.05 g/g and 0.30 g/g. The presented product provides gridded hourly moisture estimates for 1-h, 10-h, 100-h, and 1000-h fuels, essential for analyzing historical fire activity and understanding climatological trends. The methodology presented here demonstrates significant advancements in the accuracy and robustness of fuel moisture estimates, which are critical for fire forecasting and management. Full article
(This article belongs to the Special Issue Applications of Computational Statistics to Wildfire Science and Management)
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24 pages, 4089 KiB  
Article
Forest Fires: Silvicultural Prevention and Mathematical Models for Predicting Fire Propagation in Southern Italy
by Pasquale A. Marziliano, Fabio Lombardi, Maria F. Cataldo, Michele Mercuri, Salvatore F. Papandrea, Leonardo M. Manti, Silvio Bagnato, Giuseppe Alì, Pierpaolo Fusaro, Pietro S. Pantano and Carmelo Scuro
Fire 2024, 7(8), 278; https://doi.org/10.3390/fire7080278 - 7 Aug 2024
Viewed by 1222
Abstract
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 stands often exhibit high vertical and horizontal tree density, along with a significant occurrence of lying and [...] Read more.
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 stands often exhibit high vertical and horizontal tree density, along with a significant occurrence of lying and standing deadwood, thereby increasing the fuel load. On average, these pine forests are characterized by high values of above-ground biomass, ranging from 175 to 254 Mg ha−1 for the younger and the older ones, respectively. The theoretical heat energy produced per surface unit, in the case of the total combustion of the above-ground biomass, is also high, varying from 300 to 450 MJ ha−1 depending on the stage of stand development. In this study, we demonstrated the importance of silvicultural interventions in reducing the pyrological potential in pine reforested stands located in southern Italy, also giving attention to the water savings needed during extinction phases. In detail, we applied a preliminary mathematical reaction-diffusion model aimed at predicting the development of forest fires. The model was applied using data obtained through the estimation of the pyrological potential in terms of heat energy produced per surface unit (1 hectare) and the variation in the critical surface intensity. We verified that, when silvicultural interventions are applied, they induce a reduction of heat energy ranging between 17 and 21%, while the extinguishing water saved ranges between 600 and 1000 Mg ha−1. Moreover, when the silvicultural interventions are implemented, the probability of the transition from surface fire to crown fire can be reduced by up to 31%. The most effective results on fire risk mitigation are mainly obtained when thinning aimed at reducing canopy and tree density is carried out in the younger phases of the reforested pine stands. Full article
(This article belongs to the Special Issue Applications of Computational Statistics to Wildfire Science and Management)
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16 pages, 3160 KiB  
Article
A Dynamic Spatiotemporal Understanding of Changes in Social Vulnerability to Wildfires at Local Scale
by Tianjie Zhang, Donglei Wang and Yang Lu
Fire 2024, 7(7), 251; https://doi.org/10.3390/fire7070251 - 15 Jul 2024
Cited by 1 | Viewed by 1115
Abstract
Research on wildfires and social vulnerability has gained significant importance due to the increasing frequency and severity of wildfires around the world. This study investigates the dynamic changes in social vulnerability to wildfires over a decade in Idaho, USA, utilizing GIS-based tools and [...] Read more.
Research on wildfires and social vulnerability has gained significant importance due to the increasing frequency and severity of wildfires around the world. This study investigates the dynamic changes in social vulnerability to wildfires over a decade in Idaho, USA, utilizing GIS-based tools and a quasi-experimental design. We assess the evolving nature of social vulnerability at a local scale, emphasizing both spatial and temporal dynamics. Initially, we identified social vulnerability trends in relation to varying levels of wildfire risk. The research then employs propensity score matching to contrast areas affected by wildfires in 2012 with similar non-affected regions, thereby quantifying the short-term shifts in social vulnerability post-wildfires. The results indicate that regions with a high wildfire risk may display elevated vulnerability, characterized by an increase in unemployment rates and a reduction in high-income households. These findings tentatively demonstrate the compounded effect of wildfires on already vulnerable populations, highlighting the critical need for targeted interventions. Ultimately, this study underscores the importance of integrating dynamic social vulnerability assessments into wildfire management and planning, aiming to enhance community resilience and equitable resource distribution in the face of escalating wildfire threats. Full article
(This article belongs to the Special Issue Applications of Computational Statistics to Wildfire Science and Management)
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