In the field of crisis management, a swift coordination of staff with special abilities as well as the determination of the required quantity and current availability of the respective professionals is necessary to solve arising problems. In particular, schedule coordination between different authorities that do not share a calendaring system is cumbersome and can be quite time consuming, depending on the number of participants. Within the scope of this Bachelor thesis an application is developed to support the personnel planning of a safety authority.
When an appointment is created, the system determines in advance how many people from previously defined categories are required in order to ensure that the appropriate number of qualified personnel appear at the event. Participants can be easily assigned to an event from an existing contact list. If individual invitees cannot attend the event, they can be easily replaced by other persons of the respective categories.
The contacts can be imported from common contact applications, such as Google Contacts or Microsoft Outlook, and each contact be assigned to freely definable categories.
In the context of this thesis a main focus is the development of userfriendly interfaces. The herefore applied method is User-Centered Design which allows a user-based analyze of software requirements. Based on the results a low-fidelity prototype is developed, in the following refined into a high-fidelity prototype and subsequently implemented.
The bachelor thesis is about the implementation of a Data Analysis Tool for the City.Risks Project funded by the Horizon 2020 (H2020) European Research and Innovation program. The aim is to develop an interface that is easy to interact with but in the same time provides all the necessary tools to conveniently gain knowledge out of a large dataset. This is achieved by using a search engine for the implementation and various user-centered design methods to design a ser-friendly graphical user-interface that provides visualizations of the dataset as well as the possibility to search through the dataset by keywords. The underlying functionality is achieved by implementing a search engine with Lucene and Elasticsearch. The design of the user interface is realized by iteratively applying user-centered design methods like heuristic evaluation, action analysis and think-aloud.
Castaño, Luisa: Short-Term Quantitative Precipitation Forecacsting using Radar Data and Neural Networks
The short-term prediction of rainfall based on radar data is an important part of meteorology with uses that vary from flood prevention through traffic control to event planning. Modern systems allow for highly accurate approximation of the probability of rain within G hours, but not vet for the specific amount. The goal of the CIKM AnalytiCup 2017 is to improve existing systems to predict liquid precipitation quantities with 1 hour lead time by using exactly 1.5 hours of accumulated radar data.
Existing systems use computer vision algorithms to track convective cells and an empirical mathematical relation to convert the reflectivity values of these to rain rates, aside from complex physical simulations. Furthermore, they have more information available, such as wind direction and speed, temperature, atmospheric pressure, and topography. The first goal of this thesis is to solely use radar echo extrapolation data to achieve predictions with a RMSE lower than 14.G9 lnm/li. the second goal is to explore the performance of the statistical model used against a state-of-the-art Quantitative Precipitation Forecasting (QPF) system. Since the relationship between reflectivity values and the amount of rain is strongly non-linear, and given that artificial neural networks (ANN) are able to approximate any function in Mn, this thesis evaluated different reknown architectures of Convolutional Neural Networks as predictions models for the described problem. Although the set baseline was not reached, the comparison against a chosen industry-based QPF left a promising indicator for further research.