Bushfire is one of the major natural disasters that cause huge losses to livelihoods and the environment. Understanding and analyzing the severity of bushfires is crucial for effective management and mitigation strategies, helping to prevent the extensive damage and loss caused by these natural disasters. This study presents an in-depth analysis of bushfire severity in Australia over the last twelve years, combining remote sensing data and machine learning techniques to predict future fire trends. By utilizing Landsat imagery and integrating spectral indices like NDVI, NBR, and Burn Index, along with topographical and climatic factors, we developed a robust predictive model using XGBoost. The model achieved high accuracy, 86.13%, demonstrating its effectiveness in predicting fire severity across diverse Australian ecosystems. By analyzing historical trends and integrating factors such as population density and vegetation cover, we identify areas at high risk of future severe bushfires. Additionally, this research identifies key regions at risk, providing data-driven recommendations for targeted firefighting efforts. The findings contribute valuable insights into fire management strategies, enhancing resilience to future fire events in Australia. Also, we propose future work on developing a UAV-based swarm coordination model to enhance fire prediction in real-time and firefighting capabilities in the most vulnerable regions.

The 2019-20 Australia bushfire incurred numerous economic losses and significantly affected the operations of power systems. A power station or transmission line can be significantly affected due to bushfires, leading to an increase in operational costs. We study a fundamental but challenging problem of planning the optimal power flow (OPF) for power systems subject to bushfires. Considering the stochastic nature of bushfire spread, we develop a model to capture such dynamics based on Moore's neighborhood model. Under a periodic inspection scheme that reveals the in-situ bushfire status, we propose an online optimization modeling framework that sequentially plans the power flows in the electricity network. Our framework assumes that the spread of bushfires is non-stationary over time, and the spread and containment probabilities are unknown. To meet these challenges, we develop a contextual online learning algorithm that treats the in-situ geographical information of the bushfire as a 'spatial context'. The online learning algorithm learns the unknown probabilities sequentially based on the observed data and then makes the OPF decision accordingly. The sequential OPF decisions aim to minimize the regret function, which is defined as the cumulative loss against the clairvoyant strategy that knows the true model parameters. We provide a theoretical guarantee of our algorithm by deriving a bound on the regret function, which outperforms the regret bound achieved by other benchmark algorithms. Our model assumptions are verified by the real bushfire data from NSW, Australia, and we apply our model to two power systems to illustrate its applicability.

The 2019-2020 Australia bushfire season was catastrophic in the scale of damage caused to agricultural resources, property, infrastructure, and ecological systems. By the end of 2020, the devastation attributable to these Black Summer fires totalled 33 lives lost, almost 19 million hectares of land burned, over 3,000 homes destroyed and AUD $1.7 billion in insurance losses, as well as an estimated 1 billion animals killed, including half of Kangaroo Island's population of koalas (Filkov et al. 2020). According to the Australian Government Bureau of Meteorology (2021), 2019 was the warmest year on record in Australia, and the period from 2013-2020 represents eight of the ten warmest years in recorded history. There is concern and expectation that impacts of climate change - including more extreme temperatures, persistent drought, and changes in plant growth and landscape drying - will worsen conditions for extreme bushfires (CSIRO and Australian Government Bureau of Meteorology 2020; Deb et al. 2020). Contributing to the problem is that dry lightning represents the main source of natural ignition, and fires that start in remote areas deep in the temperate forests are difficult to access and monitor (Abram et al. 2021). Therefore, opportunities to detect fire ignitions, monitor bushfire spread, and understand movement patterns in remote areas are important for developing effective strategies to mitigate bushfire impact.

AWS re:Invent has ended for this year but the memories of the intriguing sessions held throughout the week are still present. Away from the multiple keynotes hosted by the conference, the virtual media briefing on the democratisation of machine-learning across Asia Pacific and Japan provided great insight into AWS' plans to make ML even more accessible. "At AWS, we believe machine-learning will be the most transformative technology of our generation. We work with more than 100,000 customers across the globe to provide them with machine-learning services for various use cases, from mitigating bushfires to accelerating COVID-19 vaccine developments, to maximising productivity for farmers." In a prior keynote, Swami Sivasubramanian, Vice President of Amazon Machine Learning at AWS discussed new AWS machine-learning announcements, some of which Chellapilla rehashed during the media session.

Backpacks to track bees, bushfire modelling and sensors to detect broken water pipes are some of the technologies being developed in Australia as part of an artificial intelligence boom. Digital Economy Minister Jane Hume says artificial intelligence has the capacity to improve the lives of all Australians. Senator Hume will tell a Committee for Economic Development of Australia event on Tuesday the government had two roles to play in terms of AI: an enabler and a standards setter, especially in terms of ethics. Earlier this year the government announced a $1.2 billion digital economic strategy which included an additional $124 million commitment to the AI initiatives. The CSIRO estimates AI technology will contribute $22 trillion into the global economy by 2030.

A decade on from the devastating Black Saturday bushfires in Victoria, Australia, we are at a point where computer simulations of community evacuations are starting to be used within the emergency services. While fire progression modelling is embedded in strategic and operational settings at all levels of government across Victoria, modelling of community response to such fires is only just starting to be evaluated in earnest. For community response models to become integral to bushfire planning and preparedness, the key question to be addressed is: when faced with a bushfire, what will a community really do? Typically this understanding has come from local experience and expertise within the community and services, however the trend is to move towards more informed data driven approaches. In this paper we report on the latest work within the emergency sector in this space. Particularly, we discuss the application of Strahan et al.'s self-evacuation archetypes to an agent-based model of community evacuation in regional Victoria. This work is part of the consolidated bushfire evacuation modelling collaboration between several emergency management stakeholders.

Estimates of koala populations have historically varied wildly. In 2016, scientists estimated there were over 300,000 koalas in Australia. In mid-2019, the Australian Koala Foundation estimated that fewer than 80,000 remained in the country, and said the number could be as low as 43,000. Concern and confusion over the koalas' numbers intensified during Australia's devastating bushfires last year, leading to news articles that the animals were "functionally extinct." But scientists challenged the accuracy of that narrative.

Perched in his fire tower high above the pine trees, Nick Dutton leans back and nods to the cascading hills and mountains behind him. "I love being out here, just away from stuff," he says. "I mean, you can't really complain." Dutton, a fire tower operator, is sitting in his office, a tiny cabin propped high above the treetops by metal supports that sway with the wind. His walls are littered with compass points and references, each a guide to the bush stretching in every direction along the eastern ACT-NSW border.

Bushfires pose a significant threat to Australia's regional areas. To minimise risk and increase resilience, communities need robust evacuation strategies that account for people's likely behaviour both before and during a bushfire. Agent-based modelling (ABM) offers a practical way to simulate a range of bushfire evacuation scenarios. However, the ABM should reflect the diversity of possible human responses in a given community. The Belief-Desire-Intention (BDI) cognitive model captures behaviour in a compact representation that is understandable by domain experts. Within a BDI-ABM simulation, individual BDI agents can be assigned profiles that determine their likely behaviour. Over a population of agents their collective behaviour will characterise the community response. These profiles are drawn from existing human behaviour research and consultation with emergency services personnel and capture the expected behaviours of identified groups in the population, both prior to and during an evacuation. A realistic representation of each community can then be formed, and evacuation scenarios within the simulation can be used to explore the possible impact of population structure on outcomes. It is hoped that this will give an improved understanding of the risks associated with evacuation, and lead to tailored evacuation plans for each community to help them prepare for and respond to bushfire.

Drone operators are being warned about rules for flying after a drone forced the grounding of firefighting aircraft battling a blaze on Tasmania's Bruny Island. Tasmania police said the aircraft had to be grounded because firefighting efforts at Conleys Point, south Bruny, were being hampered by a drone flown in the area, putting community safety at risk. "This is a reminder to all operators of drones to not fly near aircraft at any time," police said. "This puts the safety of people in aircraft at risk and also impacts on the safety of the community." Flying drones near public safety or emergency operations, such as bushfires, can be an offence under Civil Aviation Safety Authority rules.