Articles

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.

As we know, civil protection systems operates to prepare and coordinate interventions aimed to protect the population and the heritage in a risk area. These tasks are carried out both in “peace time”, for example by means of risk forecast activities and during calamity events.

A summary of: Aalto, J. and Venäläinen, A. (eds.), Climate change and forest management affect forest fire risk in Fennoscandia. (pp. 17-27). Finnish Meteorological Institute Reports 2021: 3. This report discusses the impact of climate change on forest fire management in Fennoscandia, with accelerated warming leading to increased fire risks and larger-scale fires. Effective fire prevention policies, forest management practices, and reductions in black carbon emissions are crucial for mitigating the consequences of climate change on forest fires. Proposed solutions include cooperation, enhanced tracking, and community education to improve forest fire management in the region.

The climate is quickly changing. Almost every day we are informed by television and newspapers about a natural disaster. Just to mention what happened this summer, Greece [1] is enduring the hottest July of the last 50 years due to a heatwave longer than six days. Northern Italy [2] is living in a different situation. Hail as big as tennis balls, winds as fast as a supercar. The Milan sky became dark at midday and the hinterland was affected by a tornado. The Mediterranean [11] Sea reached its highest ever recorded temperature at over 28.71c, beating the previous record of 28.25C set in 2003. The summer will still have much to say.

Information is a critical part in the organisation and execution of work in civil protection work. First responders such as firefighters collect information on emergencies to organize their work prior and during natural disasters. From traffic accidents, forest fires, floods, to rescue operations and inspections of buildings. To prepare for forest fires, climate data, terrain data, type of incident, and cause of fire are collected. The statistics are used to identify various risks that cause incidents, try to mitigate accidents, and to train and prepare appropriate responses when fires occur. With advancements in technology, remote sensing and data collection technologies can monitor, collect and analyze data efficiently to support the decision-making process prior, and during emergencies. The TEMA project will utilize existing data sources, supplemented with additional sources to create models on the further progress of forest fires and floods.

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.

In the past decade, we have witnessed spectacular breakthroughs achieved by deep learning in various applied and scientific domains. Despite their empirical success, neural networks are notorious for their black-box nature. Due to their inherent complexity, their inner workings are not readily comprehensible to human users. The emerging field of explainable artificial intelligence (XAI) aims to make the decision-making of such opaque models more transparent, improving trust and usefulness for human users. Meeting explainability standards has just become a legal requirement, in particular for high-stake applications, under the EU AI Act. In the TEMA project, we aim to employ such methods in the context of natural disaster management.

Disaster Management is important for Sustainable Development because disasters can spoil years of efforts in a few minutes. To enhance Disaster Management and Risk Reduction, the TEMA Project aims to create a platform that will help model and predict Natural Disasters evolutions and 3Dmap affected areas.