Search

California faces increasing wildfire frequency and severity, necessitating effective firefighting strategies along with well‐located infrastructure. Hydrants are vital for urban safety and must comply with established standards to adequately protect people and property. One such standard involves hydrant spacing, critical for ensuring effective emergency response in the event of a fire. Traditional geographic information systems techniques are not well suited to fully assess hydrant networks, especially in the context of spacing given complex road network geographies. This paper introduces an innovative approach to assess hydrant spacing. The developed technique is applied in the evaluation of infrastructure in Santa Barbara, California. The results reveal significant code violations along streets throughout the region, suggesting clustered pockets of vulnerability and risk. Potential strategies emerge from this analysis to enhance firefighting preparedness, offering guidance to local agencies regarding hydrant system limitations across this fire‐prone region. [ABSTRACT FROM AUTHOR]

Federal land managers in the United States are permitted to manage wildfires with strategies other than full suppression under appropriate conditions to achieve natural resource objectives. However, policy and scientific support for "managed wildfire" appear insufficient to support its broad use. We conducted case studies in northern New Mexico and southwestern Utah to examine how managers and stakeholders navigated shifting barriers and opportunities to use managed wildfire from 2018 to 2021. The use of managed wildfire was fostered through an active network of civil society partnerships in one case, and strong interagency cooperation and existing policies and plans in the other. In both, the COVID-19 pandemic, drought, and agency direction curtailed recent use. Local context shapes wildfire response strategies, yet centralized decision-making and policy also can enable or constrain them. Future research could refine the understanding of social factors in incident decision-making, and evaluation of risks and tradeoffs in wildfire response. Managers and stakeholders seeking to restore fire's ecological roles in their own landscapes through the use of managed wildfires could use these findings to cultivate supportive local environments for their objectives. Both case studies offer examples of how managed wildfires may be facilitated through civil society partnerships and interagency cooperation. Networks of civil society and agency partners can encourage policy change at multiple levels through concerted efforts over time, particularly by building a larger case through localized examples of collaborative projects and a body of regionally relevant scientific evidence. Strong interagency cooperation on both mitigation and response can also foster an environment of mutual understanding, even given differing missions and mandates for managed wildfire. Federal wildfire response must consider multiple objectives that may compete across scales, social-ecological contexts, and timeframes. These include minimizing negative impacts on human values, responding to immediate risks of fire exposure, managing land sustainably under longer timeframes; and meeting accomplishment targets, such as acres of hazardous fuels reduction, ecological restoration, and other resource objectives. Federal wildfires and land managers are permitted to manage wildfires for natural resource objectives but face challenges of ambiguous terminology, conflicting policies, drought, increasing numbers of homes in wildlands, and unanticipated events, such as the COVID-19 pandemic. Conditions, opportunities, and barriers to manage wildfire vary substantially by locality and are dependent on local actors, yet also subject to higher-level changes in policy direction. Beyond improved risk analytics and decision support tools, enabling social and internal institutional conditions may also facilitate opportunities for use of managed wildfire. Social science can provide evidence and frameworks including concrete lessons learned, expanded use of after-action reviews, process monitoring, briefings with leadership, and science application through boundary-spanning organizations. [ABSTRACT FROM AUTHOR]

• Leverage social media data to assess wildfire response effort. • Apply the SIR model to quantitative measure disaster response on Twitter. • Reveal the disparity of wildfire response across different regions. • Illustrate a connection between Twitter activity and wildfire propagation. • The proposed topic-based SIR model aids in bolstering community resilience. Effective disaster response is critical for communities to remain resilient and advance the development of smart cities. Responders and decision-makers would benefit from reliable, timely measures of the issues impacting their communities during a disaster, and social media offers a potentially rich data source. Social media can reflect public concerns and behaviors during a disaster, offering valuable insights for decision-makers to understand evolving situations and optimize resource allocation. We used Bidirectional Encoder Representations from Transformers (BERT) topic modeling to cluster topics from Twitter data. Then, we conducted a temporal-spatial analysis to examine the distribution of these topics across different regions during the 2020 western U.S. wildfire season. Our results show that Twitter users mainly focused on three topics: "health impact," "damage," and "evacuation." We used the Susceptible-Infected-Recovered (SIR) theory to explore the magnitude and velocity of topic diffusion on Twitter. The results displayed a clear relationship between topic trends and wildfire propagation patterns. The estimated parameters obtained from the SIR model in selected cities revealed that residents exhibited a high level of several concerns during the wildfire. Our study offers a quantitative approach to measure disaster response and support community resilience enhancement. [ABSTRACT FROM AUTHOR]

Given the explosive growth of information technology and the development of computer vision with convolutional neural networks, wildfire field data information systems are adopting automation and intelligence. However, some limitations remain in acquiring insights from data, such as the risk of overfitting caused by insufficient datasets. Moreover, most previous studies have only focused on detecting fires or smoke, whereas detecting persons and other objects of interest is equally crucial for wildfire response strategies. Therefore, this study developed a multilabel classification (MLC) model, which applies transfer learning and data augmentation and outputs multiple pieces of information on the same object or image. VGG-16, ResNet-50, and DenseNet-121 were used as pretrained models for transfer learning. The models were trained using the dataset constructed in this study and were compared based on various performance metrics. Moreover, the use of control variable methods revealed that transfer learning and data augmentation can perform better when used in the proposed MLC model. The resulting visualization is a heatmap processed from gradient-weighted class activation mapping that shows the reliability of predictions and the position of each class. The MLC model can address the limitations of existing forest fire identification algorithms, which mostly focuses on binary classification. This study can guide future research on implementing deep learning-based field image analysis and decision support systems in wildfire response work.

The performance of wildfire suppression is often monitored using statistics related to area burned and time to contain a fire. Potential factors affecting the probability of initial attack (IA) success and the probability of large fires were examined in a data set composed of 334 Australian wildfires that burned in forest and shrubland vegetation and used aerial- and tanker-based suppression during the IA phase. Logistic regression analysis was used to determine the most significant predictor variables for a range of area- and time-based definitions for these measures. The variables that were found to be the best predictors of IA success were fire area at IA, fuel hazard, and aircraft response time. The probability of large fires was related to fuel hazard, area at IA, and the Forest Fire Danger Index. Fire area at IA was strongly linked with aerial suppression time delay and was also influenced by weather and fuel hazard score. Fire management practices can influence IA area, response timing, and fuel hazard. IA area and response times can be minimized through efficient fire detection and by deploying appropriate suppression resources rapidly from bases in locations that provide optimized geographical coverage. Fuel hazard can be moderated through management actions such as fuel reduction burning [ABSTRACT FROM AUTHOR]

Given the explosive growth of information technology and the development of computer vision with convolutional neural networks, wildfire field data information systems are adopting automation and intelligence. However, some limitations remain in acquiring insights from data, such as the risk of overfitting caused by insufficient datasets. Moreover, most previous studies have only focused on detecting fires or smoke, whereas detecting persons and other objects of interest is equally crucial for wildfire response strategies. Therefore, this study developed a multilabel classification (MLC) model, which applies transfer learning and data augmentation and outputs multiple pieces of information on the same object or image. VGG-16, ResNet-50, and DenseNet-121 were used as pretrained models for transfer learning. The models were trained using the dataset constructed in this study and were compared based on various performance metrics. Moreover, the use of control variable methods revealed that transfer learning and data augmentation can perform better when used in the proposed MLC model. The resulting visualization is a heatmap processed from gradient-weighted class activation mapping that shows the reliability of predictions and the position of each class. The MLC model can address the limitations of existing forest fire identification algorithms, which mostly focuses on binary classification. This study can guide future research on implementing deep learning-based field image analysis and decision support systems in wildfire response work. [ABSTRACT FROM AUTHOR]