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The increase in both the frequency and magnitude of natural disasters, coupled with recent advancements in artificial intelligence, has introduced prospects for investigating the potential of new technologies to facilitate disaster response processes. Preliminary Damage Assessment (PDA), a labor-intensive procedure necessitating manual examination of residential structures to ascertain post-disaster damage severity, stands to significantly benefit from the integration of computer vision-based classification algorithms, promising efficiency gains and heightened accuracy. Our paper proposes a Vision Transformer (ViT)-based model for classifying damage severity, achieving an accuracy rate of 95%. Notably, our model, trained on a repository of over 18,000 ground-level images of homes with damage severity annotated by damage assessment professionals during the 2020–2022 California wildfires, represents a novel application of ViT technology within this domain. Furthermore, we have open sourced this dataset—the first of its kind and scale—to be used by the research community. Additionally, we have developed a publicly accessible web application prototype built on this classification algorithm, which we have demonstrated to disaster management practitioners and received feedback on. Hence, our contribution to the literature encompasses the provision of a novel imagery dataset, an applied framework from field professionals, and a damage severity classification model with high accuracy. [ABSTRACT FROM AUTHOR]

In California, wildfires caused by electrical infrastructure have left the state's investor-owned power utilities with major and growing liabilities. But even in such an incendiary environment, the financial industry has demonstrated that it can profit from disaster. This paper uses the 2019-2020 bankruptcy of Pacific Gas & Electric to explain how. In it, I show how "risk" in California's electricity industry is legally constituted, mediated, and allocated. First, I explain how financial perceptions of wildfire risk in California's electricity industry are shaped by the state's legal and regulatory environment, and how the law is used to manage this risk. I then turn to PG&E's bankruptcy to show how litigation functions as a financial strategy. In court, risk is endogenous to legal-financial practice. I develop the concepts of "legal arbitrage" and "leverage" to explain how law mediates the relationship between risk and finance. I adapt the concept of legal arbitrage to show how financial assets (like those in a utility with unprecedented wildfire liabilities) can possess both legal and market value, which can and often do diverge in circumstances of distress. I use the term leverage to refer to a particular kind of legal-financial power which enables actors to transfer risk away from themselves and onto others. In working through these concepts, I argue that that mainstream perceptions of risk in the financial industry are inadequate, especially in an era of increasing climate insecurity. [ABSTRACT FROM AUTHOR]

The number of wildfire incidents affecting communities in Wildland–Urban Interface (WUI) areas has been rapidly increasing. Understanding the fire spread between structures and evaluation of the response of the communities to the possible wildfire scenarios are crucial for proper risk management in the existing and future communities. This paper discusses a stochastic methodology to evaluate the community's response to potential wildfire scenarios. The methodology has three primary features: (1) it is based on stochastic modeling of fire spread; (2) it breaks the wildfire incident into two consecutive segments: spread inside the wildland and spread inside the community; (3) it integrates the two spread models in the form of a conditional probability. The paper focuses on fire spread inside the community and applies the proposed methodology to two case studies in California, US. The two case studies demonstrate variations in fire spread within the communities for the given fire scenarios approaching from the wildland. The performance of communities is characterized using cumulative distribution functions of the number of ignited buildings over time. [ABSTRACT FROM AUTHOR]

Some of the deadliest wildfires in the U.S., such as California’s 2018 wildfires, have been ignited by power systems. In an effort to prevent and minimize the ignition of wildfires, or control them if ignited, energy companies have developed wildfire mitigation plans. This paper provides energy companies and power system operators, engineers, researchers, and suppliers an overview of the state-of-the-art studies that address key topics in these wildfire mitigation plans and compares the wildfire mitigation plans of several energy companies. The key topics include grid design and system hardening, asset management and inspection, situational awareness and forecasting, operational response, vegetation management, public safety power shutoff, and risk-spend efficiency. This paper also presents a comparison of several energy companies’ decision-making criteria for initiating a public safety power shutoff. Finally, we discuss opportunities for future research studies that could help energy companies prevent wildfire ignitions. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between daily carbon emissions for California's savanna and forest wildfires and regional meteorology over the past 18 years. For each fuel type, the associated weather [daily maximum wind, daily vapor pressure deficit (VPD), and 30-day-prior VPD] is determined for all fire days, the first day of each fire, and the day of maximum emissions of each fire at each fire location. Carbon emissions, used as a marker of wildfire existence and growth, for both savanna and forest wildfires are found to vary greatly with regional meteorology, with the relationship between emissions and meteorology varying with the amount of emissions, fire location, and fuel type. Weak emissions are associated with climatologically typical dryness and wind. For moderate emissions, increasing emissions are associated with higher VPD from increased warming and only display a weak relationship with wind speed. High emissions, which encompass ∼85% of the total emissions but only ∼4% of the fire days, are associated with strong winds and large VPDs. Using spatial meteorological composites for California subregions, we find that weak-to-moderate emissions are associated with modestly warmer-than-normal temperatures and light winds across the domain. In contrast, high emissions are associated with strong winds and substantial temperature anomalies, with colder-than-normal temperatures east of the Sierra Nevada and warmer-than-normal conditions over the coastal zone and the interior of California. Significance Statement: The purpose of this work is to better understand the influence of spatially and temporally variable meteorology and spatially variable surface fuels on California's fires. This is important because much research has focused on large climatic scales that may dilute the true influence of weather (here, high winds and dryness) on fire growth. We use a satellite-recorded fire emissions dataset to quantify daily wildfire existence and growth and to determine the relationship between regional meteorology and wildfires across varying emissions in varying fuels. The result is a novel view of the relationship between California wildfires and rapidly variable, regional meteorology. [ABSTRACT FROM AUTHOR]

This paper proposes a modeling approach for characterizing the probability of wildfire ignition caused by faults on power distribution systems. The proposed model serves as a starting point in research literature to illustrate, from an analytical perspective, the many factors that influence wildfire ignitions in power distribution systems. This paper presents the series of events that leads to power-system-related wildfire ignitions, and characterizes the wildfire ignition probability as a combination of the probability that a fault occurs along a power distribution line segment, and the probability that the fault results in the sustained ignition of a vegetation fuel bed surrounding the line. The proposed model integrates a variety of data including environmental conditions, power system protection settings, and power system line flows. A case study is performed on a test 33-bus distribution system using observed historical weather data from a high-threat fire district in California. The California case study is utilized to investigate the effects of three primary factors (wind speed, line congestion, and protection settings) on wildfire ignition probability. [ABSTRACT FROM AUTHOR]

Forest fires cause every year damages to biodiversity, atmosphere, and economy activities. Forest fire simulation have improved significantly, but input data describing fire scenarios are subject to high levels of uncertainty. In this work the two-stage prediction scheme is used to adjust unknown parameters. This scheme relies on an input data calibration phase, which is carried over following a genetic algorithm strategy. The calibrated inputs are then pipelined into the actual prediction phase. This two-stage prediction scheme is leveraged by the cloud computing paradigm, which enables high level of parallelism on demand, elasticity, scalability and low-cost. In this paper, all the models designed to properly allocate cloud resources to the two-stage scheme in a performance-efficient and cost-effective way are described. This Cloud-based Urgent Computing (CuCo) architecture has been tested using, as study case, an extreme wildland fire that took place in California in 2018 (Camp Fire). • Data-driven calibration to deal with uncertainty in forest fire spread prediction. • Cloud-based urgent computing implementation of a two-stage prediction model. • Use of utility function to deal with the cost-performance trade-off. • Validation against a deadly and destructive wildfire with promising results. [ABSTRACT FROM AUTHOR]

Wildfires are natural or man‐made disasters that continuously threaten portions of the transmission and distribution grid, and thus the stability of the electric grid. This paper presents a two‐stage framework for assessing power system‐wildfire risk using a data‐driven wildfire prediction model. The first stage of the framework estimates the spatio‐temporal probability of potential wildfire ignition and propagation using a deep neural network in combination with the wildfire physical spread model. Analysis reveals similar spatial and temporal patterns between the model‐predicted wildfire ignition potential and actual wildfire ignition. Motivated by these observations, the second stage assesses the wildfire risk in the power grid operation in terms of potential loss of load by de‐energisation, through combining geospatial information system data of the power grid topology and the stochastic spatio‐temporal wildfire model developed in the first stage. The electric power utility applications introduced by the proposed framework are twofold: 1) a spatio‐temporal risk model for proactive de‐energisation against potential power system failure‐induced wildfire, and 2) a spatio‐temporal spreading model for optimal grid operations against exogenous wildfire. The proposed model, based on real‐world dataset, is demonstrated on the IEEE 24‐bus test system mapped to a study area in Northern California, while the results illustrate the proposed model can achieve the best performance in potential wildfire ignition detection (AUC of 0.995) compared to other baselines, as well as demonstrates the risk‐aware operation of the power system enabled by the proposed framework. [ABSTRACT FROM AUTHOR]

Post-wildfire investigations of structures provide engineers with data to understand how to harden building infrastructure for future wildfire hazards. This paper presents a preliminary framework where field-collected LiDAR and drone data is integrated with numerical models for post-wildfire assessment of buildings with the goal of learning from wildfire-damaged buildings. The developed framework provides a new and novel approach for post-wildfire instigations of buildings using field-collected data, particularly LiDAR and drone data. This framework is demonstrated on a metal-framed school building in Paradise, California that was heavily damaged in the 2018 Camp Fire. The authors collected drone aerial images and light detection and ranging (LiDAR) scans of Achieve Charter High School after the 2018 Camp Fire. All data were used to create a system-level finite element model to simulate the fire behavior of a portal frame with non-prismatic members. A parametric study was performed with three typical compartment fires to examine the influence of building characteristics on the fire's maximum temperature and duration. Several time–temperature (T–t) curves, representing different fire scenarios, were generated between the two extreme cases produced by the parametric study. Finally, the finite element model was subjected to the generated fire curves, and the results were compared with the measured deformations from the LiDAR data collected in the field. The finite element model, subjected to each fire Time–temperature curve scenario, simulated global deformations within 2% of the true deformations. [ABSTRACT FROM AUTHOR]

As wildfires in the United States are becoming more frequent and severe, mitigating the risk of wildfire ignitions from power line faults is an increasingly crucial effort. Long-term ignition prevention strategies, especially converting overhead lines to underground cables, are expensive. Thus, it is important to prioritize upgrades on lines that will reduce wildfire ignition risk the most. However, since so many factors contribute to ignition risk, it is difficult to quantify the wildfire risk associated with power lines. This paper examines how various risk definitions based on historical wildfire risk maps can be used to inform transmission upgrade planning. These risk metrics are evaluated using an optimization model that determines which overhead lines should be undergrounded such that the total wildfire risk in the network is minimized. The risk assignment and upgrade selection are tested on both a synthetic network and the actual transmission lines in California. • Addresses risk of electric power line faults leading to wildfire ignitions. • Proposes a methodology to quantify wildfire risk of electric power lines. • Leverages current wildfire risk maps and estimates ignition probability from historical trends. • Identifies optimal set of lines to underground to reduce wildfire risk. [ABSTRACT FROM AUTHOR]

The wildland-urban interface (WUI) is defined as a geographic area where human developments and flammable vegetation merge in a wildfire-prone environment. Losses due to wildfire have been rising in the past decade, attributed to changes in vegetation growth, fuel availability, and increased land developments in WUI. This paper studies the process of wildfire spread inside WUI communities. The fire spread rate within WUI communities is determined for nine wildfires that were ranked among the most destructive wildfires in North America. An improved quasi-empirical model that considers radiation and fire spotting as modes of fire spread inside a community is proposed. The new model is validated using the documented spread rates during the 2007 Witch and Guejito fires and the 2017 Tubbs fire. The proposed model is computationally efficient and can be used to quantify fire spread rate and the number of affected structures inside a community during a wildfire event. • Communities are being exposed to faster spreading wildfires in recent years. • A streamlined integrative urban fire spread model is proposed. • The proposed model is applied to two case studies in California. • The proposed model is capable of capturing fire progression in WUI communities when compared to real scenarios. [ABSTRACT FROM AUTHOR]