Inno_MAUS – Innovative Instrumente zum Management des Urbanen Starkregenrisikos
Funded by BMBF
Prof. Dr. Axel Bronstert, University of Potsdam
University of Potsdam (Hydrology and Climatology: Prof. Dr. Bronstert, Dr. Heistermann, Geography & Disaster Risk Research: Prof. Dr. Thieken), TUM (Chair of Hydraulic and Water Resources Engineering: Prof. Dr. Rüther, Chair of Data Science in Earth Observations: Prof. Dr. Zhu), KISTERS AG, Orbica UG
Project Scientists from Our Chair:
Dr. Yilei Shi, Yueli Chen, Qingsong Xu, Wei Huang, Yue Zeng
2022 – 2024
Extreme rainfall events pose a common problem that municipalities need to deal with. Due to the increased probability of extreme weather events due to Climate Change, however, immediate action is key to prevent massive damage or to mitigate risks. We respond to this challenge by facilitating cutting-edge data science to manage the risks going along with heavy rainfall events in a holistic fashion. We develop and train machine learning algorithms to process precipitation data in combination with remote-sensing information on the topography and urban morphology, to quantify very generally the effectiveness of drainage and water retention in the urban environment.