Research topics
The Research Topics investigated by the members of the World Alliance on Digitalization for Disaster & Emergency Management, span over a broad range of domains in various disciplines. In the following, examples of research topics are given under the following classifications:
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Disaster and emergency management:
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Awareness, disaster and emergency detection and monitoring: Aims to detect the effects of disasters efficiently and effectively, such as the damages made, conditions of humans and living creatures, etc.
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Demand modeling and generation: Based on the observed damages and the environmental conditions, demand lists are automatically inferred for the necessary resources so that aid operations can be executed efficiently and effectively. Demands for resources can be various, including rescue operations, fire fighters, ambulances, security forces, repair teams, shelters, transport vehicles for evacuation, heating and cooking facilities, food and water, etc.
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Optimization and scheduling : The available resources are optimally assigned to the inferred demands on time. In case of insufficient resources, prioritization, trade-off and/or dynamic selection techniques are applied.
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Performance measurement: The performance indicators of operations are defined formally so that the desired objectives can be specified and measured. Accordingly, ongoing operations can be monitored online and in case of deviations from the desired performance values, corrective actions can be executed.
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Tracking and control: This phase includes both monitoring, evaluation and controlling actions of the ongoing activities as well as coordination among the aid operations.
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Simulation and game playing: To determine the effect of a large set of prospective disaster scenarios, determine the effectiveness and efficiency of the disaster and emergency ecosystem platform, evaluate the necessary quantity of resources and to optimize the locations of logistics centers, simulation environments and techniques must be researched, designed and implemented. Even in times when no disasters are experienced, triggered by the simulated disaster scenarios, the platform must be continuously operational so that it can be optimized using online machine learning techniques.
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​​Computation: Realization of the disaster and emergency management system is not trivial and requires research in various
computer science disciplines: For example:
Event-based computation, Digital twins, Communication networks, Coordination, Platforms and Software Engineering Aspects.
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Sociological disaster research: It is the of the social relations amongst both natural and human-made disasters.
Awareness: Disaster and emergency detection and monitoring
We define disaster situation awareness as the ability of the authorities to effectively and efficiently detect the negative effects of disasters so that aid operations can be planned and executed in a timely manner. In general, the objective is twofold: To understand the type and magnitude of the damage caused and to determine the conditions and locations of the persons that need help.
Examples of research topics are:
Modelling and deriving different kinds of disasters based on core models. Examples of such models can be for example, GIS based topologies, disaster cause models (such as fault lines, sea levels, water ways, etc.), conditions of buildings, ground mechanics, structure of urbanization, etc.
Prediction of disaster and emergency conditions.
Modeling and interfacing a large set of sensors and data sources, such as: Airborne data sources: UAVs, swarm of UAVS, airplanes, Ultrasonic sensors, Space borne data sources: Satellites, GPS trackers, Base stations, Mobile applications, Collapse detectors, Seismic sensors, Water leakage sensors, Gas detectors, Fire and smoke detectors, Camera-based detectors (optical, infrared, etc.), Motion detectors, Face recognizers, People counters, Utility meters (electricity, gas, water), Various kinds of sensors for flooding, Microphones, CO2 meters, Microwave radars, Registration databases, Mobile analysis LABs.
Determining effectiveness and efficiency of the data sources based on disaster and emergency detection criteria. For example:
Maximizing the detection of the effect of disasters and emergency conditions in static and changing conditions
Maximizing the accuracy of the location of alive humans and other relevant living creatures
Minimizing costs
Minimizing the speed of processing (keeping under the timing deadlines)
Sensor and data fusion analysis and design environments
Synthesis op optimal sensor and data fusion configurations
Pattern recognition/classification techniques for different detection types and objectives
Adaptable strategies and mechanisms
Stream processing, complex event processing
Embedded and distributed systems for data gathering and strorage
Edge computing techniques for data processing
Storage of data and big data analytics
Sensor and data source ontologies
IoT networks
Data gathering for improving data processing techniques
Reconfigurable topologies
Machine learning techniques in improving the effectiveness and efficiency in data processing
Standardization of sensor and data source interfaces and communication and processing interfaces and protocols