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Mobile Crowd Sensing of Water Level to Improve Flood Forecasting in Small Drainage Areas

Abstract : Flood forecasting is particularly difficult and uncertain for small drainage basins. One reason for this is due to inadequate temporal and spatial hydrological input variables for model-based flood predictions. Incorporating additional information collected by volunteers with the help of their smartphones can improve flood forecasting systems. Data collected in this way is often referred to VGI data (Volunteered Geographic Information data). This paper discusses how this information can be incorporated into a flood forecasting system to support flood management in small drainage basins on the basis of mobile VGI data. It therefore outlines the main functional components involved in such a VGI-based flood forecasting platform while presenting the component for mobile data acquisition (mobile sensing) in more detail. In this context, relevant measurement variables are first introduced and then suitable methods for recording these data with mobile devices are described. The focus of the paper lies on discussing various methods for measuring the water level using inbuilt smartphone sensors. For this purpose, three different image-based methods for measuring the water level at the banks of small rivers using a mobile device and the inbuilt orientation and camera sensors are explained in detail. It is shown that performing the measurements with the user’s help via appropriate user interaction and utilising known structures at the measuring points results in a rather robust image-based measurement of the water level. A preliminary evaluation of the methods under ideal conditions found that the developed measurement techniques can achieve both an accuracy and precision of less than 1 cm.
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Submitted on : Thursday, August 2, 2018 - 9:50:28 AM
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Simon Burkard, Frank Fuchs-Kittowski, Anna Bhroithe. Mobile Crowd Sensing of Water Level to Improve Flood Forecasting in Small Drainage Areas. 12th International Symposium on Environmental Software Systems (ISESS), May 2017, Zadar, Croatia. pp.124-138, ⟨10.1007/978-3-319-89935-0_11⟩. ⟨hal-01852636⟩



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