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A basic interpolation of water levels, derived from the locations and water depths reported by Tweets, was used as a starting point. Several improvements over this simple method were evaluated. For example flooded areas that were not directly connected to any of the observations were removed from the flood map. Additionally the effect of grouping observations that belonged to the same flooded areas, either based on the vicinity of observations or common cells downstream of the observations, was investigated. A last method focussed on using the cells that lay downstream of observations, called the downstream flow paths of observations, to interpolate water levels along, see figure below. Also the use of Tweets that did not mention a water depth, by giving them a default water depth, was reviewed. Instead of using a digital terrain model to produce the flood maps, a height above nearest drainage map was used in this research, which reduced the risk of downstream overestimations of water level.
Interpolation along downstream flowpaths
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York modeled flood water depth (left) zoomed in to the city center (center) and flood probability (right)
Conclusions
The resulting flood maps can be applied to both pluvial and fluvial floods. The scale at which the flood maps can be used varies from case to case, depending on the topographical characteristics of the area, which determine to what extent errors in the input datasets propagate to the flood maps. For areas with high terrain slopes maps at fine scale can be produced, delineating flood extents to within 50 m of their actual location, whereas for more flat areas only conclusions can be drawn at more coarse scale, because deviations of 500 m in flood extent are not uncommon. Although the real-time application of the flood maps could not be fully reviewed, since the datasets used in this research contained too few observations and Tweets were not processed automatically, the computational time of the methods used to create the flood extent and uncertainty estimates, allows for application in real-time
Current methods to create flood inundation maps from Twitter messages have been improved. The method that uses interpolating of water levels along flow paths produced the best results. This method improved upon the basic interpolation of water levels by using the flow paths downstream of observations to determine which observations belong to the same continuously flooded area. Further improvements are made by first interpolating the water levels along these flow paths, instead of directly interpolating them throughout the entire area and subsequently excluding flooded areas which are not directly connected to the downstream flow paths of observations.
The degree of uncertainty caused by errors in the input dataset depends largely on the topographical characteristics of the area and can be large for flat areas with low terrain slopes. Mainly locational errors of Tweets and errors in the elevation data affect these locations. Since fluctuations in water levels have less of an effect in areas with steep terrain slopes, the uncertainty in these areas remains relatively limited. Also the uncertainties caused by errors in the water depth mentioned by the Tweets and default water depth added to observations without a water depth was found to have only a minor influence on the uncertainty in flood extentThe analysis of the time variation in the number of Tweets indicated that the severity of flooding was quite accurately reflected in the number of Tweets, there were too few Tweets in the datasets constructed for this research to do a thorough analysis of time variation. For the Jakarta case however, the dataset was intentionally reduced in size, since the Tweets had to be manually analysed. Although the flood mapping methods used in this research, given their limited computational time, allowed for real-time application, also the manual process of extracting locations and water depths from the Tweets, should be automated to make this possible. Additionally the process of creating uncertainty maps should be further optimized, since these do not accurately reflect the degree of uncertainty caused by locational errors and density of observations. For cases such as the York case, for which only a small amount of relevant Tweets was found, further methods to generate and find more relevant observations should be reviewed. If these issues are addressed however, the real-time flood maps and uncertainty maps created using Tweets have the potential of providing a wealth of information to for example rescue workers or other persons requiring flood information in real-time, where current methods such as hydraulic models and remote sensing are lacking.