This article explores how command systems represent a cornerstone in large scale emergency management based on the example of the German Regulation DV 100.
The articles seeks to illustrate the methodology used for the elaboration of the risk map, starting from cartographic bases, in order to obtain a product in raster format that allows rapid spatial analysis in a GIS environment for processing. It is based on the work previously presented "Methodology and Processing of the Model for the Forest Fire Risk Map".
In the past decade, deep learning has made remarkable advancements in various fields, but neural networks are often considered black boxes due to their complex inner workings. The emerging field of explainable artificial intelligence (XAI) seeks to make these models more transparent, complying with the new legal requirements under the EU AI Act. The TEMA project aims to apply XAI methods to natural disaster management, potentially enhancing prediction accuracy and assisting emergency responders in making informed decisions.
This article explores how technology can improve natural disaster management in Finland, making a connection with European areas that, like Finland, were historically less bound to experience natural disaster but that are now facing issues as consequence of climate change.
To effectively address the multifaceted challenges posed by climate change, it is crucial to comprehend its various dimensions. In particular, the impact of climate change is closely linked to water, accounting for 90% of its consequences. The extremes of excessive water, resulting in flooding, and insufficient water, causing drought, have a substantial impact on both food security and water availability. Therefore, a thorough understanding of water dynamics becomes imperative for effective adaptation and mitigation strategies.
Deep neural networks have shown great empirical success in the last years, but their inner working often remained elusive. XAI methods aim to open up the black box of such opaque models at the heart of many modern machine learning and artificial intelligence applications.
Wildfires, with their devastating impact on ecosystems and communities, present a formidable challenge in natural disaster management. In response, a pioneering approach has been developed, synergizing satellite imagery, rover exploration, and sophisticated algorithms
