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Digital Terrain Models (DTMs) represent an essential source of information that can allow the behaviour of the urban floodplain, and its interactions with the drainage system, to be examined, understood and predicted. Typically, such data are obtained via Light Detection and Ranging (LiDAR). If a DTM does not contain adequate representation of urban features the results from the modelling efforts can be. This is due to the fact that urban environments contain variety of features, which can have functions of storing and/or diverting flows during flood events. The work described in this paper concerns further improvements of a LiDAR filtering algorithm which was discussed in a previous work. The key characteristics of this improved algorithm are: ability to deal with buildings, detect elevated road and represent them accordance to reality and deal with bridges and riverbanks. The algorithm was tested using a real-life data from a case study of Kuala Lumpur. The results have shown that the newly developed MPMA2 algorithm has better capabilities of identifying some of the features that are vital for urban flood modelling applications than any of the currently available algorithms and it leads to better agreement between simulated and observed flood depths and flood extents.

Improved methodology for processing raw LiDAR data to support urban flood modelling – accounting for elevated roads and bridges