The effect of global warming is triggering natural disasters to occur more often and of greater severity, prompting the need for enhanced disaster management measures. Particularly dangerous to people's lives, property, and economy are floods, as recent devastating events in Germany and other countries have shown. Static flood forecast maps are a primary tool used in conventional flood management methods. However helpful, these maps frequently lack real-time data integration and are dependent on assumptions. This study addresses these shortcomings by introducing a cloud-based platform that uses dynamic Geographic Information Systems (GIS) and enhanced flooding modeling to improve catastrophe preparedness and response.

Near-real-time flood simulations are produced by the suggested method, which combines high-performance computers, real-time sensor data, and weather forecasts. This makes it possible for decision-makers to evaluate risks, visualize changing flood situations, and plan efficient actions. The platform offers an integrated and interactive environment for stakeholders to assist all aspects of disaster management, including mitigation, readiness, response, and recovery.

A communication platform that promotes data sharing and cooperation between first responders and other stakeholders, is one of the system's essential elements. To provide a thorough and current operating picture, it integrates information from social media, sensors, and geographic data. With the help of the flood simulation component, which is based on cutting-edge hydrological models and real-time data, users may plan evacuation routes, assess potential effects, and forecast the course of flooding. The platform offers a new degree of operational efficiency during emergencies thanks to its near-real-time functioning, which is a major improvement over conventional technique.

The implementation of this technology at various stages of flooding occurrences is also examined in this article. Through 2D and 3D simulations, the technology helps with data collecting and analysis prior to flooding, providing reliable flood forecasts. The platform's high-performance processing capabilities enable quick simulation updates during flooding, giving decision-makers vital information. The technology helps with recovery planning and damage assessment after floods by helping to coordinate reconstruction operations and visualizing the effects of the storm.

All things considered, a holistic approach to catastrophe management is provided by the combination of dynamic GIS, real-time flooding simulation, and sophisticated communication technologies. By improving situational awareness and decision-making, this technology eventually helps mitigate the negative effects of disasters on communities. The significance of mentioned aspects helps in building more resilient communities capable of successfully handling the challenges posed by disasters resulting from climate change is emphasized in the end of this paper.

Incoherence in coordination between institutions that address climate
change adaptation (CCA), disaster risk reduction (DRR) and food security (FS)
policy areas exacerbates FS challenges in most developing countries, including
South Africa. To address such incoherencies, this article seeks to develop a
coordination mechanism that could help in aligning FS functions undertaken by
CCA, DRR and FS institutions. The coordination mechanism was developed
using aspects from the functional resonance analysis method (input, time,
preconditions, resources, control and output) and data gathered through policy
analysis, key informant interviews, and written responses. The coordination
mechanism allows for simultaneous participation of relevant stakeholders
through a systematic characterisation of FS functions. It allows policymakers
and practitioners to identify variabilities, challenges, and opportunities that
should be considered before any FS activity is executed.

The passability of a terrain is understood as the possibility of traversing it cross-country, outside the regular road network, taking into account weather and soil conditions. The analysis of passability is mainly applicable in the planning of military operations, which very often take place in roadless areas. The issue of passability is also very relevant in the emergency management process, especially in less developed areas, where there is a need for emergency vehicles to reach facilities located away from the regular road network via roadless roads.

The presentation will outline the factors to be taken into account in the process of passability modelling and the system being developed at the Military University of Technology for the automated generation of terrain passability maps, which can also be used in the crisis management process. In presented project, the problem of terrain classification to the respective category of passability was solved (among others) by applying artificial neural networks to generate (calculate) the Index of Passability (IOP). The main methodological assumption of the conducted research was to refer the index of passability of the terrain to the primary fields of various shapes and sizes.

The basis for calculating IOP are elements of land cover, weather and soil type that exist in the given primary field. The results show a comprehensive analysis of the reliability of the neural network parameters, considering the number of neurons, learning algorithm, activation functions and input data configuration. The studies and tests carried out have shown that a well-trained neural network can automate the process of terrain classification in terms of passability conditions. The Authors assumed that the values of indices of passability obtained with use of the algorithms may differ, even if the same methods and source data are used, depending on the type of the primary field used, i.e., its shape and size. Considering the above, the Authors analyzed the influence of the shape and size of the primary field on the results of automated terrain classification for the purposes of developing passability maps. The Authors determined indices of passability for square primary fields of various side lengths ranging from 1 m to 10 km. The Authors has demonstrated that terrain classification for passability purposes may also be performed with use both: military and civilian data sources.

What is important developed system makes enable of using terrain passability maps for generating graphs that enable the determination of the optimum route between two points. The proposed methodology enables the determination of different variants of routes: a longer route that passes all terrain obstacles or a route that is shorter but more difficult to pass.

The results obtained allow the conclusion to be drawn that the modelling of terrain passability allows the rescue operation to be planned more efficiently, which in the case of emergency management can be crucial to the success of the overall operation.