Articles

Ever since there have been forests, there have been wildfires spreading through them. With changing climates also come increasing risks of wildfires. The year 2022 was the second worst year on record in Europe from 2006 to 2022 according to the European Forest Fires Information Service, and 2021 and 2023 have also had cumulative burnt areas above the average of this time period.

Wildfires are a natural disturbance that shape the ecosystems and landscapes (Pausas and Keeley 2009) although they are a growing threat to the environment, economical assets and population in many regions worldwide (Molina-Terrén et al. 2019). In fact, substantial amounts of financial resources have been invested in fire management aiming to reduce the damage associated with unintended consequences of wildfires and improve safety for the population (Cardil and Molina 2015; Stocks and Martell 2016). Fire agencies rely on suites of wildfire analysis products that are designed to meet the needs of operational response and mitigation planning. Given the challenges that first responders and decision-makers were faced with to adequately predict fire spread and potential impacts, and dispatch the most appropriate resources every time a wildfire is detected in California, California Department of Forestry and Fire Protection (CAL FIRE) (2019), through a request for innovative ideas in March 2019, selected Wildfire Analyst Enterprise (WFA-e) to evaluate initial attack fires and calculate wildfire risk based on automatic simulations.

As a result of climate and other environmental changes, on which scientists have long raised awareness, European Member States are experiencing increasingly frequent and devastating natural disasters, ranging from forest fires to floods. A prime example is Greece’s recent tragic experience. Within just 3 months (from July to September 2023), the country was severely hit by various devastating wildfires and floods; the most recent of them being Evros forest fires and Thessaly floods (natural disasters that caught the attention of local, national, as well as international media).

Data is often considered the lifeblood of machine learning algorithms. The availability of vast amounts of data has played a vital role in the rapid development of AI applications across various domains. However, a significant challenge that researchers and practitioners face in the AI landscape is the issue of data scarcity. Data scarcity refers to the limited availability or inadequacy of high-quality data for training AI models.

The immense wildfires that recently devastated northeastern Greece have underscored the critical need for advanced tools to monitor and manage such ecological disasters. As of August 28th, a staggering 80,000 hectares were decimated in the Evros region, marking the largest wildfire in Greece in over two decades.

Energy utilities across the United States continue to face the challenge of reducing wildfire risk while also increasing their reliability as part of their ongoing operations. As droughts and weather events accelerate the spread of wildfire to more and more communities, we are seeing that this risk is not just a “California issue” anymore. Not only do risk managers at energy utilities need to know the probability of a wildfire, but they also need to know the consequence to their assets and service area from one ignition compared to another. In short, not all ignitions (fires) are created equal. Risk managers need the tools to understand the likelihood of a utility-cased ignition as they balance their overall wildfire risk.

Airborne means play a critical role in the management of natural emergencies. The collection of information for building prediction and behaviour modes, the transportation of material and brigades, search & rescue missions, or the assessment of damages are just a few examples. In the last decade, the development of drone technology has unprecedently widened the range of potential applications and uses in emergency management and particularly, in emergency monitoring. The increase of frequency and severity of natural emergencies originated by the climatic change require our best technologies reduce their effect and mitigate their impact. Horizon Europe TEMA project is aware of this challenge and addresses it by extensively combining novel AI and Big Data systems with a wide variety of complementary platforms for data collection. Aerial robots play a critical role in TEMA project making use of fleets of aerial robots as powerful and highly responsive, flexible, and reconfigurable tools to collect information useful for the monitoring, measuring, and assessment of natural emergency situations.

In June 2021, Germany experienced the biggest natural disaster in its recent history due to flash floods that hit Western Germany. Experts say that floods like this occur once in 1000 years. Two days before the flood, a warning was issued by the German Weather Service (DWD) predicting 80 to 180 litres of rain per square meter, but that amount was exceeded the following night with 200 litres per square meter. Hot and dry weather in the weeks prior to the floods dried the soil and reduced its capacities to absorb the rainwater, which further intensified the floods in many areas. Places in North Rhine-Westphalia and Rhineland-Palatinate were particularly affected.

Since the emergence of the first camera and radio systems over a century ago, media systems have played an essential part in our society. Not only for entertainment purposes, but also for documentation, communication, analysis, and training. Especially during disasters and emergencies, media has become a primary tool transporting information, documenting situations, planning responses, as well as predicting problems and allowing preemptive decision-making.

The utilization of novel advancements such as Artificial Intelligence (AI), extensive datasets (big data), and three-dimensional visualizations is being observed. In recent years, the rise of various innovative forecasting approaches, including the utilization of big data, has significantly bolstered disaster management for a wide range of natural hazards. These approaches leverage cutting-edge technologies and interdisciplinary collaborations to enhance the accuracy and timeliness of hazard predictions, thereby minimizing the potential impact on vulnerable communities.