High-Density LiDAR Data Analysis for Urban Flood Assessment

Urban areas are facing growing challenges associated with climate change, necessitating the adoption of advanced tools such as Lidar technology and Geographic Information System (GIS) to effectively assess and mitigate these issues. The aim of this study focuses on the use of such tools to generate high-resolution terrain representations, including Digital Terrain Models (DTM) and Digital Surface Models (DSM), and to conduct a comprehensive hydrological analysis of the Meisino Park in Turin, Italy, an area historically susceptible to flooding events. Lidar data, provided by the SDG11LAB laboratory of the Polytechnic School of Turin (DIST), have been used to produce precise digital elevation models by capturing elevation data for thousands of points across the terrain, enabling highly accurate analysis, 1m/pixel. Given that the Lidar instrument cannot penetrate the water surface, supplementary data on the river area have been collected from different sources to obtain a complete digital elevation model. As a result, the first focus of the study is the statistical validation of the terrain models obtained. Statistical measures such as Root Mean Square Error (RMSE) and Normalized Median Absolute Deviation (NMAD) have been used to ensure the accuracy of the results obtained. An additional analysis has been conducted to extract urban features present in the terrain model, such as buildings and roads. These data have been extracted from both Lidar points and online databases, such as the Geoportal website of the Piedmont region. The ultimate refinement of feature extraction has been achieved by conducting a supervised classification process using ENVI software. The final step of this thesis is focused on the hydrological analysis, which encompasses the generation of hydrographic basins of a subset of the area under study using the DTM with the highest accuracy obtained. Various precipitation scenarios, ranging from low-intensity rainfall (2 mm/h) to extreme events (200 mm/h), are considered to evaluate the area's hydrological response under different climatic conditions. Additionally, the study examines the impact of varying street inlet clogging conditions on discharge calculations for each sub-catchment, highlighting specific street inlets that are particularly susceptible to inundation across different precipitation and clogging scenarios. The findings of this study will reveal a notable increase in surface flow when a higher number of street inlets are clogged, and higher precipitation intensity scenarios are considered.