A Methodology for Processing Raw LiDAR Data to Support Urban Flood Modelling Framework: Case Study—Kuala Lumpur Malaysia

High quality representation of the topographic and the correct representation of significant urban features would be a fundamental foundation to a better urban flood model. Without such a representation, simulation of flood behaviours would be less successful as the flow patterns were completely dependent on ground levels and the shape of the features. Typically, such data can be obtained via Light Detection and Ranging (LiDAR) surveys. The process of turning raw LiDAR data into a useful Digital Terrain Model (DTM) involves careful processing and application of thinning, filtering and interpolation algorithms. Filtering is a process of automatic detection and interpretation of bare earth and objects from the point cloud of LiDAR data, which results in the generation of a DTM. To date, many filtering algorithms have been developed, and in a more general sense, many of them have become standard industry practice. However, when it comes to the use of a DTM for urban flood modelling applications, these algorithms cannot be always considered suitable. Depending on the terrain characteristics, they can even lead to misleading results and degrade the predictive capability of the modelling technique. This is largely due to the fact that urban environments often contain a variety of features (or objects) such as buildings, elevated roads, bridges, curbs and others which have the ability to store or divert flows during flood events. As these objects dominate urban surfaces, appropriate filtering methods need to be applied in order to identify such objects and to represent them correctly within a DTM so that the DTM can be used more safely in modelling applications. The work described in this chapter concerns improvements of a LiDAR filtering algorithm. The key characteristics of this improved algorithm are: ability to recover curbs and the use of appropriated roughness coefficient of Manning's value to represent close-to-earth vegetation (e.g. grass and small bush). The results of the improved algorithm were demonstrated using Kuala Lumpur (Malaysia) as a case study. Improvement, in terms of a difference in flood depths and flood flows were observed between the hydraulics models built from several available filtering algorithms and the improved algorithm (MPMA). The overall results suggest that the improvement made in MPMA can lead to some difference in model results, which may in some cases be significant with a tendency towards incorrect flood flow by those models in which such features are not properly represented.