Real Time Prediction of Pluvial Floods and Induced Water Contamination in Urban Areas (EVUS)
|Förderung durch:||BMBF Georisiken|
This project aims at developing a fast forecast model for pluvial floods in the city of Hannover.
The main goal of the subproject for ikg is to integrate new sensors for the flood prediction models. This includes the combination of crowd-sourcing information for the localization and description of the pluvial floods incidents, e.g., the information gathered by smart phone apps or the social networks such as Twitter. A smart phone app will be developed with the cooperation from IP SYSCON. These collected data are then used as the essential inputs for the flood modeling in other subprojects. A data infrastructure will be built to collect, store, and visualize the flood relevant data.
Continuous observation of the wiper activities from the moving cars and the analysis of usage of streets based on GPS-trajectories of the cars are also the rainfall-indicators, which are expected to be combined with the flood prediction models.
Another aspect of this subproject is the acquisition of high density (cm-range) and high accuracy (2-5cm) Digital Terrain Models (DTM) of the roads using Mobile Mapping Systems for the surface flow modeling. The selected region for this research is located in Ricklingen Hannover. With the Mobile Mapping System, the measurement of this area has already been finished in December, 2015. Further, the Mobile Mapping data will be used for the DTM generation as well as the road relevant objects extraction such as the inlets and curbstones.
Finally, the flood prediction results and relevant information from all the subprojects will be visualized in a web-based GIS application and open for the public and authorities.
Weitere Infos finden Sie hier.
Feng, Y. and Schlichting, A. and Brenner, C. (2016): 3D feature point extraction from LiDAR data using a neural network, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XLI-B1, pp. 563-569
Yu Feng and Monika Sester (2017): Social media as a rainfall indicator, Bregt, A., Sarjakoski, T., Lammeren, R. van, Rip, F. (Eds.). Societal Geo-Innovation : short papers, posters and poster abstracts of the 20th AGILE Conference on Geographic Information Science | datei |
Feng, Y. and M. Sester (2018): Extraction of pluvial flood relevant volunteered geographic information (VGI) by deep learning from user generated texts and photos, ISPRS International Journal of Geo-Information 7(2),39
Fuchs, L. Graf, T., Haberlandt, U. Kreibich, H., Neuweiler, I. Sester, M. Berkhahn, S. Feng, Y. Peche, A., Rözer, V., Sämann, R.; Shehu, B., Wahl, J. (2018): Echtzeitvorhersage urbaner Sturzfluten und damit verbundene Wasserkontaminationen, AquaUrbanica 2018, Schriftenreihe Wasser Infrastruktur Ressourcen, Band 1, TU Kaiserslautern. | datei |
Fuchs, L. Graf, T., Haberlandt, U. Kreibich, H., Neuweiler, I. Sester, M. Berkhahn, S. Feng, Y. Peche, A., Rözer, V., Sämann, R.; Shehu, B., Wahl, J. (2018): Echtzeitvorhersage von Überflutung, Schadstofftransport und Schäden für Sturzflutereignisse am Beispiel Oberricklingen in Hannover, Forum für Hydrologie und Wasserbewirtschaftung, Heft 40, 2018 | datei |