Potential of Twitter derived flood maps: comparing interpolation methods and assesing uncertainties

Brouwer, T. (2016) Potential of Twitter derived flood maps: comparing interpolation methods and assesing uncertainties.

Abstract:Globally, floods cause large damages and a huge number of casualties each year. During floods there are a number of parties that benefit from having high quality flood information at their expense. For example rescue workers can use information about the severity of flooding to choose which areas to target and which routes to take in an area. After floods there is also a need for flood information, which can for example help insurance companies in evaluating flood damages, aid organizations in targeting rebuilding efforts or local governments in evaluating flood risk. As a result of the increasing number of floods, caused by phenomena such as urbanization and climate change, there is an increasing demand for accurate and timely flood information. Traditionally this information is produced in the form of flood maps either generated using hydraulic models or remote sensing. However hydraulic models often need detailed schematizations of the study area, require large amounts of input data and can take considerable computational time. For remote sensing data the time it takes from an observations being made, to the release of the data is considerable and this data often has a low temporal resolution. These drawbacks, which particularly affect the potential of these methods for real-time applications, in combination with the rise of social networks over the last decade, have triggered the search for a new way of creating flood maps, using these social media. The growth of social networks over the last decade has led to huge amounts of data, potentially containing valuable information about flooding, being available almost instantly. Several studies already looked into using this data, which either used it as auxiliary data for other methods to create flood maps or used the data to create flood maps directly. None of these studies however focussed on comparing different methods to create flood maps from social media data, or assessed the uncertainties in maps created using social media data. Therefore the objective of this research was to establish a preferred method of estimating flood extents from Twitter data and assess the uncertainties and applicability of the maps created using this method. Specifically Twitter data was used since it is openly and freely available. To achieve this objective, the research included a comparison of different ways of applying interpolation to create flood maps from the Twitter data, an assessment of the uncertainty in flood extent and a variety of analyses to investigate in what context the Twitter derived flood maps can be applied. Therefore two case studies of recent floods in Jakarta (Indonesia) and York (United Kingdom) were evaluated. For both case studies a dataset of Tweets was constructed, from which both locations and water depths were derived. Also a digital terrain model at 20 m resolution was constructed for both case studies. The flood extents created for the Jakarta case study were validated using information derived from photographs and the flood extents created for the York case study were validated using actual recorded flood extents. As a first step the sets of Tweets collected for these cases and the locations and water depths derived from them, were investigated. The time variation in the number of Tweets in these datasets was reviewed by comparing it to the time variation in measured water levels. Also the magnitude of errors in location and water depth derived from the Tweets was investigated by comparing them to locations and water depths derived from photographs attached to some of the Tweets. Besides analysing the Tweets gathered for both case studies, different methods of creating flood maps were evaluated. 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. 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. Using the method that created the most accurate flood maps, the uncertainty in flood extent, resulting from locational errors of Tweets, errors in water depths mentioned by Tweets and errors in the elevation data, was evaluated using Monte Carlo simulations. Also the effects of choosing a different default water depth value and using different resolutions on the uncertainty in flood extent was investigated. The comparison of the flood extents generated using the different flood mapping methods with validation datasets showed that for both the Jakarta and York case studies, the best results were obtained by interpolating water levels along the flow paths downstream of observations. The flood extent calculated for Jakarta covered 75% of the validation points and a comparison of the flood extents calculated for York with recorded flood extents showed that the area of the flood extent that was correct, made up 69% of the total flood extent gotten by merging the created and recorded flood extents. Although two different validation methods were used, making it hard to compare both case studies, the quality of the flood extents varied between the cases. It was seen that in more flat areas, such as downstream Jakarta, flood extents were less precise than in areas with more slopes, such as the inner-city of York. These differences in topography also affected the degree to which errors in the datasets caused uncertainties in flood extent. These uncertainties were especially high in flatter areas, which were mainly affected by locational errors of Tweets and errors in the elevation data. In areas with steeper slopes, these errors caused considerably less uncertainty and at all locations the uncertainty caused by errors in the water depth specified by the messages, or default water depth used for messages that did not mention one, was only minor. Given these large differences in uncertainties, the scale at which maps could be produced varied from fine for the inner-city of York for which flood extents were delineated to within 50 m of their actual location, to more coarse in flatter areas such as downstream Jakarta, where deviations of up to 500m were not uncommon. Although the 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.
Item Type:Essay (Master)
Faculty:ET: Engineering Technology
Subject:56 civil engineering
Programme:Civil Engineering and Management MSc (60026)
Link to this item:http://purl.utwente.nl/essays/71007
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