利用移动车辆传感器数据获取实时降水信息

Dimitri Falk, Adrian Treis, M. Braun, M. Hoffmann, E. Costa-Patry
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摘要

当今一代的机动车辆配备了许多传感器来收集各种传感器数据。这些传感器主要用于内部控制各种舒适和安全功能,但也有可能在外部跨部门应用中使用,例如水管理用例。目前,以Emscher和Lippe地区为例,车载传感器数据对降水信息实时集聚的一般适用性是mobileVIEW研究项目的关键问题之一。为了能够对降水数据进行时空调整,在实施过程中面临的一个主要挑战是从车辆传感器的测量中获得有效的降水值。为此,使用历史降水雷达数据训练的决策树,从与降雨相关的测量数据和基于车辆位置的附加环境值的组合中得出降水强度。在基于Delft-FEWS的实际洪水预警系统中,分析了径流预报和暴雨临近预报的新潜力。
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Ableitung von Echtzeit-Niederschlagsinformationen aus mobilen Kfz-Sensordaten / Derivation of Real-Time Precipitation Information Using Mobile Vehicle Sensor Data
Today’s generation of motor vehicles is equipped with numerous sensors collecting a variety of sensor data. These sensors are primarily designed to internally control miscellaneous comfort and safety functions but are also offering potential to be used externally in cross-sectoral applications, such as water management use cases. At this point, the general suitability of vehicle sensor data for realtime agglomeration of precipitation information using the example of Emscher and Lippe region is one of the key issues of the research project mobileVIEW. A major challenge in implementation in order to be able to conduct spatio-temporal adjustments of precipitation data is to derive valid precipitation values from vehicle sensor measurements. For this purpose, a decision tree trained with historical precipitation radar data is used to derive precipitation intensities from a combination of rain related measurements with additional environmental values based on the position of the vehicle. Emerging potential for runoff forecasting and heavy rainfall nowcasting is analysed within the operational flood early warning system based on Delft-FEWS.
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来源期刊
AGIT- Journal fur Angewandte Geoinformatik
AGIT- Journal fur Angewandte Geoinformatik Earth and Planetary Sciences-Computers in Earth Sciences
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