Flexigas Simulator Architectuur (versie 1.0)

Doel van dit document is om een (high level) architectuur te beschrijven aan de hand waarvan binnen het project Flexigas een simulator voor een Smart Biogas Grid ontwikkeld kan worden. Het document is primair geschreven voor de ontwikkelaars van de simulator, maar zal ook gebruikt worden in de discussie met de beoogde eindgebruikers. In het project Flexigas (waarin wordt samengewerkt in verschillende thema´s1 met diverse partners) bouwt TNO kennis op op het gebied van een Smart Biogas Grid. In de volgende paragraven wordt meer achtergrond informatie gegeven over het Flexigas project en het specifieke Thema A (ketenoptimalisatie) waarin TNO werkzaam is. Het voorliggende document is een eerste versie van de afgesproken deliverable: A3.1 Architectuur rapportage. In verschillende iteraties wordt het document aangepast en/of uitgebreid

Signal analysis and anomaly detection for flood early warning systems

We describe the detection methods and the results of anomalous conditions in dikes (earthen dams/levees) based on a simultaneous processing of several data streams originating from sensors installed in these dikes. Applied methods are especially valuable in cases where lack of information or computational resources prohibit computing the state of the dike with finite element and other mathematical models. The data-driven methods are part of the artificial intelligence (AI) component of the ‘Urbanflood’ early warning system. This AI component includes pre-processing (e.g., gap filling and measurements synchronization procedures) of data streams, feature extraction and anomaly detection by one-side (also known as one-class) classification methods. Our approach has been successfully validated during a non-destructive piping experiment at the Zeeland dike (The Netherlands)

Signal Processing Methods for Flood Early Warning Systems

We present in a data-driven approach for detection of anomalies in earthen dam (dike) behaviour that can indicate the onset of flood defence structure failure. This approach is implemented in the UrbanFlood early warning system's Artificial Intelligence component that processes dike measurements in on-line manner. Suggested approach successfully detected anomalies registered during the non-destructive piping experiment at the Zeeland dike (the Netherlands)

Flood early warning system: design, implementation and computational modules

AbstractWe present a prototype of the flood early warning system (EWS) developed within the UrbanFlood FP7 project. The system monitors sensor networks installed in flood defenses (dikes, dams, embankments, etc.), detects sensor signal abnormalities, calculates dike failure probability, and simulates possible scenarios of dike breaching and flood propagation. All the relevant information and simulation results are fed into an interactive decision support system that helps dike managers and city authorities to make informed decisions in case of emergency and in routine dike quality assessment. In addition to that, a Virtual Dike computational module has been developed for advanced research into dike stability and failure mechanisms, and for training the artificial intelligence module on signal parameters induced by dike instabilities. This paper describes the UrbanFlood EWS generic design and functionality, the computational workflow, the individual modules, their integration via the Common Information Space middleware, and the first results of EWS monitoring and performance benchmarks