The heavy rainfall event in August 2002 in Saxony/Germany caused the break of a flood retaining basin in a valley of the Erzgebirge Mountains. The rainfall event with ists hyrologic characteristics and dam break event were analysed and an outflow hydrograph was determing. The propagation of the dam break flood wave in the valleyas of the Briesnitz and Müglitz Rivers has been simulated. Calculated values were compared with some observed data.

In August 2002 a heavy rainfall event lasting more than two days occurred in Saxony/Germany. This led to extreme flash floods and extreme high water levels in some left tributaries of the river Elbe in the Ore mountains (Erzgebirge). Much damage occurred: houses and bridges were destroyed, much sediment moved and a dam broke. About 20 people died. Immediately after the event a mapping exercise of all damages arising from the flood was compiled. The hydrologic and hydraulic processes during the flood were analysed. This analysis included the genesis of the flood, the flood routing, the erosion, plain bed load transport and the sedimentation. The analysis of such an extraordinary event can help us learn how to prevent flood damages in the future.

The heavy rainfall event in August 2002 in Saxony/Germany caused the break of a flood retaining basin in a valley of the Erzgebirge Mountains. The rainfall event with ists hyrologic characteristics and dam break event were analysed and an outflow hydrograph was determing. The propagation of the dam break flood wave in the valleyas of the Briesnitz and Müglitz Rivers has been simulated. Calculated values were compared with some observed data.

In August 2002 a heavy rainfall event lasting more than two days occurred in Saxony/Germany. This led to extreme flash floods and extreme high water levels in some left tributaries of the river Elbe in the Ore mountains (Erzgebirge). Much damage occurred: houses and bridges were destroyed, much sediment moved and a dam broke. About 20 people died. Immediately after the event a mapping exercise of all damages arising from the flood was compiled. The hydrologic and hydraulic processes during the flood were analysed. This analysis included the genesis of the flood, the flood routing, the erosion, plain bed load transport and the sedimentation. The analysis of such an extraordinary event can help us learn how to prevent flood damages in the future.

Das Finden von zuverlässigen Informationen über gegebene Ereignisse aus großen und dynamischen Textsammlungen, wie dem Web, ist ein wichtiges Thema. Zum Beispiel sind Rettungsteams und Versicherungsunternehmen an prägnanten Fakten über Schäden nach Katastrophen interessiert, die heutzutage online in Web-Blogs, Zeitungsartikeln, Social Media etc. zu finden sind. Solche Fakten helfen, die erforderlichen Hilfsmaßnahmen zu bestimmen und unterstützen deren Koordination. Allerdings ist das Finden, Extrahieren und Aggregieren nützlicher Informationen ein hochkomplexes Unterfangen: Es erfordert die Ermittlung geeigneter Textquellen und deren zeitliche Einordung, die Extraktion relevanter Fakten in diesen Texten und deren Aggregation zu einer verdichteten Sicht auf die Ereignisse, trotz Inkonsistenzen, vagen Angaben und Veränderungen über die Zeit. In dieser Arbeit präsentieren und evaluieren wir Techniken und Lösungen für jedes dieser Probleme, eingebettet in ein vierstufiges Framework. Die angewandten Methoden beruhen auf Verfahren des Musterabgleichs, der Verarbeitung natürlicher Sprache und des maschinellen Lernens. Zusätzlich berichten wir über die Ergebnisse zweier Fallstudien, basierend auf dem Einsatz des gesamten Frameworks: Die Ermittlung von Daten über Erdbeben und Überschwemmungen aus Webdokumenten. Unsere Ergebnisse zeigen, dass es unter bestimmten Umständen möglich ist, automatisch zuverlässige und zeitgerechte Daten aus dem Internet zu erhalten. / Finding reliable information about given events from large and dynamic text collections, such as the web, is a topic of great interest. For instance, rescue teams and insurance companies are interested in concise facts about damages after disasters, which can be found today in web blogs, online newspaper articles, social media, etc. Knowing these facts helps to determine the required scale of relief operations and supports their coordination. However, finding, extracting, and condensing specific facts is a highly complex undertaking: It requires identifying appropriate textual sources and their temporal alignment, recognizing relevant facts within these texts, and aggregating extracted facts into a condensed answer despite inconsistencies, uncertainty, and changes over time. In this thesis, we present and evaluate techniques and solutions for each of these problems, embedded in a four-step framework. Applied methods are pattern matching, natural language processing, and machine learning. We also report the results for two case studies applying our entire framework: gathering data on earthquakes and floods from web documents. Our results show that it is, under certain circumstances, possible to automatically obtain reliable and timely data from the web.