A methodology for processing raw LiDAR data to support urban flood modelling framework

Abstract

An assessment has been carried out to study the performance of seven different LiDAR filtering algorithms and to evaluate their suitability for urban flood modelling applications. It was found that none of these algorithms can be regarded as fully suitable to support such work in its present form. The paper presents the augmentation of an existing Progressive Morphological filtering algorithm for processing raw LiDAR data to support a 1D/2D urban flood modelling framework. The existing progressive morphological filtering algorithm was modified to incorporate buildings with basement, passage buildings and solid buildings and its value was demonstrated on a case study from Kuala Lumpur, Malaysia. The model results were analysed and compared against recorded data in terms of flood depths, flood extents and flood velocities. The difference in flood depths of approximately 40% was observed between a model that uses a DTM modified by the progressive morphological filtering algorithm and the predictions of other models. The overall results suggest that incorporation of building basements within the DTM can lead to a significant difference in the model results with a tendency towards overestimation for those models which do not incorporate such a feature.