Effective disaster response is critical for affected communities. 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 demands 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 details how the SIR model and topic modeling using social media data can provide decision-makers with a quantitative approach to measure disaster response and support their decision-making processes.

As California faces its first major heat wave of the summer this Fourth of July weekend, state officials are urging residents to not become complacent about the threat of wildfires this year. Standing beneath the blistering sun at the Grass Valley Air Attack Base in Nevada County, California Department of Forestry and Fire Protection chief Joe Tyler outlined the state's plans to battle blazes this year with new tools and technology, as well as increased vegetation management efforts. He cautioned that while the wet start to 2023 may have delayed the start of fire season, it has not deterred it. "The abundant rain has produced tall grass and other vegetation that's dried out already and is ready to burn," Tyler said. Additionally, portions of the state are expected to soar into the triple digits this weekend, including up to 110 degrees in the Sacramento Valley.

This paper proposes a generalised and robust multi-factor Gated Recurrent Unit (GRU) based Deep Learning (DL) model to forecast electricity load in distribution networks during wildfire seasons. The flexible modelling methods consider data input structure, calendar effects and correlation-based leading temperature conditions. Compared to the regular use of instantaneous temperature, the Mean Absolute Percentage Error (MAPE) is decreased by 30.73% by using the proposed input feature selection and leading temperature relationships. Our model is generalised and applied to eight real distribution networks in Victoria, Australia, during the wildfire seasons of 2015-2020. We demonstrate that the GRU-based model consistently outperforms another DL model, Long Short-Term Memory (LSTM), at every step, giving average improvements in Mean Squared Error (MSE) and MAPE of 10.06% and 12.86%, respectively. The sensitivity to large-scale climate variability in training data sets, e.g. El Ni\~no or La Ni\~na years, is considered to understand the possible consequences for load forecasting performance stability, showing minimal impact. Other factors such as regional poverty rate and large-scale off-peak electricity use are potential factors to further improve forecast performance. The proposed method achieves an average forecast MAPE of around 3%, giving a potential annual energy saving of AU\$80.46 million for the state of Victoria.

Wildfires are increasingly impacting the environment, human health and safety. Among the top 20 California wildfires, those in 2020-2021 burned more acres than the last century combined. California's 2018 wildfire season caused damages of $148.5 billion. Among millions of impacted people, those living with disabilities (around 15% of the world population) are disproportionately impacted due to inadequate means of alerts. In this project, a multi-modal wildfire prediction and personalized early warning system has been developed based on an advanced machine learning architecture. Sensor data from the Environmental Protection Agency and historical wildfire data from 2012 to 2018 have been compiled to establish a comprehensive wildfire database, the largest of its kind. Next, a novel U-Convolutional-LSTM (Long Short-Term Memory) neural network was designed with a special architecture for extracting key spatial and temporal features from contiguous environmental parameters indicative of impending wildfires. Environmental and meteorological factors were incorporated into the database and classified as leading indicators and trailing indicators, correlated to risks of wildfire conception and propagation respectively. Additionally, geological data was used to provide better wildfire risk assessment. This novel spatio-temporal neural network achieved >97% accuracy vs. around 76% using traditional convolutional neural networks, successfully predicting 2018's five most devastating wildfires 5-14 days in advance. Finally, a personalized early warning system, tailored to individuals with sensory disabilities or respiratory exacerbation conditions, was proposed. This technique would enable fire departments to anticipate and prevent wildfires before they strike and provide early warnings for at-risk individuals for better preparation, thereby saving lives and reducing economic damages.

Drought, extreme temperatures and thousands of lightning bolts each day led to the ignition of wildfires across Alaska's interior. More than 2.4 million acres have burned this year by wildfires, which is double the acreage that is typically scorched at this point in the state's wildfire season. The Alaska wildfire season typically begins in late May and ends in late July, and the National Park Services states that, on average, one million acres burn statewide each year. The blazes are being ignited by lightning strikes plaguing the state - nearly 25,000 bolts were detected between June 28 and July 4 and more than 10,000 have hit since then. There are only about 1,000 firefighters in the interior of the state who are tirelessly working around the clock to put out more than 225 fires, which are forcing hundreds of residents from their homes.

LOS ANGELES, July 22 (Thomson Reuters Foundation) - Last summer, as Will Harling captained a fire engine trying to control a wildfire that had burst out of northern California's Klamath National Forest, overrun a firebreak and raced towards his hometown, he got a frustrating email. It was a statistical analysis from Oregon State University forestry researcher Chris Dunn, predicting that the spot where firefighters had built the firebreak, on top of a ridge a few miles out of town, had only a 10% chance of stopping the blaze. "They had spent so many resources building that useless break," said Harling, who directs the Mid Klamath Watershed Council, and works as a wildland firefighter for the local Karuk Tribe. "The index showed it had no chance," he told the Thomson Reuters Foundation in a phone interview. The Suppression Difficulty Index (SDI) is one of a number of analytical tools Dunn and other firefighting technology experts are building to bring the latest in machine learning, big data and forecasting to the world of firefighting.

Last summer, as Will Harling captained a fire engine trying to control a wildfire that had burst out of northern California's Klamath National Forest, overrun a firebreak, and raced towards his hometown, he got a frustrating email. It was a statistical analysis from Oregon State University forestry researcher Chris Dunn, predicting that the spot where firefighters had built the firebreak, on top of a ridge a few miles out of town, had only a 10% chance of stopping the blaze. "They had spent so many resources building that useless break," said Mr. Harling, who directs the Mid Klamath Watershed Council, and works as a wildland firefighter for the local Karuk Tribe. "The index showed it had no chance," he told the Thomson Reuters Foundation in a phone interview. The Suppression Difficulty Index (SDI) is one of a number of analytical tools Mr. Dunn and other firefighting technology experts are building to bring the latest in machine learning, big data, and forecasting to the world of firefighting.

"The JAIC is working to bring critical AI detection technology to the first responders who bravely battle wildfires. Increased use of AI will reduce response timelines, increase situational awareness, and save more American lives." Last year's California wildfire season was the deadliest and most destructive in United States history. More than 8,500 fires burned across nearly 1.9 million acres in the state of California and resulted in more than $16.5 billion in damage. Cumulatively, the wildfires were the costliest natural disaster of 2018, as well as one of the deadliest.