FLUD is a state-of-the-art urban flood monitoring and forecasting platform which relies on the following technologies in order to deliver its benefits
Advanced Computational Intelligence
Accumulated Knowledge from a variety of diverse inputs
Effective visualization through state of the art GUI and VR environments
Robust sensor design
Warning and optimized recommendations
FLUD utilizes a novel approach that is based on deep learning
techniques for spatial temporal modeling. This approach
is the base for developing intelligent Big Data analysis and
decision support systems for deployment in a variety of
industrial and commercial application areas. IC approach
executes two main functions enabling users to make more
informed decisions regarding matters of importance:
Accurate analysis and modeling of historical data Precise forecasting of future events based on the analysis.
IC novel spatial temporal approach is capable of studying complex data in terms of both space and time, thus enabling users to gain better insights about cause-effect relationships. This approach analyzes and models Big Data in real-time, in competitive computational times in order to forecast future outcomes and predict abnormalities in the data, based on the analysis of deviation patterns. IC approach is a robust methodology able to handle uncertainties and noise affecting a process, and features reduced processing times, and increased efficiency and productivity for users.
For FLUD, IC deep learning approach will be combined with state of the art Fuzzy Logic (FL) techniques in order to provide accurate and timely predictions. FL enables FLUD to incorporate expert opinion into the decision-making process of the model. FL is able to combine various input sources, quantify qualitative information, and account for uncertainty in providing accurate forecast and classification results. More importantly, FL is able to deliver its results fast. As it is widely acknowledged FL can produce the same results as other more complex techniques but faster.
The application of IC novel CI techniques enable FLUD to
incorporate and account for information from a variety of
input sources including:
FLUD platform enables its user to visualize effectively the
incoming information and the forecasting results generated
by the computational models.
FLUD also includes a virtual construct of the area of deployment where the end user is able to navigate in the UTM area, monitor the rain as it unfolds, and be informed for any warnings related to increased urban flooding probability.
The following videos show draft versions of FLUD’s VR control environment, and UTM’s virtual construct.
During extreme flooding phenomena sensory equipment is tested to
its limits. Therefore, as part of the FLUD project the
consortium has designed custom sensors able to collect and
transmit data under extreme weather conditions.
During extreme weather phenomena the mobile network is down so there has to be alternative ways for warning the public. FLUD takes this reality into account and utilizes new communication technologies and protocols to deliver timely warning to the public Given data related to the local authorities are provided FLUD is also able to provide optimized recommendation related to the allocation of human and public resources to effectively tackle a hazardous developing situation. FLUD achieves this through the deployment of advanced optimization algorithms. These algorithms mimic the theory of evolution to calculate the parameters that provide the optimal solution for a given problem.