Data-driven methods in application to flood defence systems monitoring and analysis

Modern society fully depends on properly functioning energy, communication and other types of systems. Natural or technological hazards can cause great losses if these systems are functioning improperly. Condition monitoring of objects protecting us from possible hazards becomes nowadays more and more important problem. The problem of the flood defence structures condition monitoring is considered in this thesis. Analysis of levee behaviour using the measurements collected from the sensors installed inside the dams provides the early warning indicators. Available time before the possible levee collapse can be used for mitigation of possible effects of the flood. The goal of this work was to make a new step in investigation of the concepts of levee condition monitoring for providing on-line alerting. We developed a data-driven approach for levee condition monitoring. We demonstrated that it is possible to monitor the levee behaviour using the data-driven models independently and in combination with physical modelling. The developed approach can be used by the domain experts as an alerting tool in order to reduce time for decision making

Machine learning methods for environmental monitoring and flood protection

More and more natural disasters are happening every year: floods, earthquakes, volcanic eruptions, etc. In order to reduce the risk of possible damages, governments all around the world are investing into development of Early Warning Systems (EWS) for environmental applications. The most important task of the EWS is identification of the onset of critical situations affecting environment and population, early enough to inform the authorities and general public. This paper describes an approach for monitoring of flood protections systems based on machine learning methods. An Artificial Intelligence (AI) component has been developed for detection of abnormal dike behaviour. The AI module has been integrated into an EWS platform of the UrbanFlood project (EU Seventh Framework Programme) and validated on real-time measurements from the sensors installed in a dike

An Approach for Real-time Levee Health Monitoring Using Signal Processing Methods

AbstractWe developed a levee health monitoring system within the UrbanFlood project funded under the EU 7th Framework Programme. A novel real-time levee health assessment Artificial Intelligence system is developed using data-driven methods. The system is implemented in the UrbanFlood early warning system. We present the application of dedicated signal processing methods for detection of leakage through the water retaining dam and subsequent analysis of the measurements collected from one of the UrbanFlood pilot levees at the Rhine river in Germany