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- Improving a DSM Obtained by Unmanned Aerial Vehicles for Flood ModellingPublication . Mourato, Sandra; Fernandez, Paulo; Pereira, Luísa; Moreira, MadalenaAccording to the EU flood risks directive, flood hazard map must be used to assess the flood risk. These maps can be developed with hydraulic modelling tools using a Digital Surface Runoff Model (DSRM). During the last decade, important evolutions of the spatial data processing has been developed which will certainly improve the hydraulic models results. Currently, images acquired with Red/Green/Blue (RGB) camera transported by Unmanned Aerial Vehicles (UAV) are seen as a good alternative data sources to represent the terrain surface with a high level of resolution and precision. The question is if the digital surface model obtain with this data is adequate enough for a good representation of the hydraulics flood characteristics. For this purpose, the hydraulic model HEC-RAS was run with 4 different DSRM for an 8.5 km reach of the Lis River in Portugal. The computational performance of the 4 modelling implementations is evaluated. Two hydrometric stations water level records were used as boundary conditions of the hydraulic model. The records from a third hydrometric station were used to validate the optimal DSRM. The HEC-RAS results had the best performance during the validation step were the ones where the DSRM with integration of the two altimetry data sources.
- Geostatistical analysis of settlements induced by liquefaction: case study river Lis Alluviums, PortugalPublication . Veiga, Anabela; Mourato, Sandra; Rodrigues, HugoIn the present work the result of the application of geostatistical methods to soil settlement data is presented. The settlements are induced by liquefaction as a result of an earthquake of magnitude 5.5. In the present paper the ArcGIS Geostatistical Analyst software was used, where a number of Kriging methods are available. Geostatistical analysis was performed on two phases: (i) modelling the semivariogram to analyse the surface properties and; (ii) application of a Kriging method. The best adjustment was obtained with a Gaussian model with a first order function trend removal. The settlement values were obtained from the analysis and treatment of results of SPT tests carried out on soils corresponding to alluvial soils in the urban centre of Leiria, Portugal. The results show a significant area where are expected large settlements that can generate significant damages in the building stock and infrastructures.
- A new approach for computing a flood vulnerability index using cluster analysisPublication . Fernandez, Paulo; Mourato, Sandra; Moreira, Madalena; Pereira, LuísaA Flood Vulnerability Index (FloodVI) was developed using Principal Component Analysis (PCA) and a new aggregation method based on Cluster Analysis (CA). PCA simplifies a large number of variables into a few uncorrelated factors representing the social, economic, physical and environmental dimensions of vulnerability. CA groups areas that have the same characteristics in terms of vulnerability into vulnerability classes. The grouping of the areas determines their classification contrary to other aggregation methods in which the areas' classification determines their grouping. While other aggregation methods distribute the areas into classes, in an artificial manner, by imposing a certain probability for an area to belong to a certain class, as determined by the assumption that the aggregation measure used is normally distributed, CA does not constrain the distribution of the areas by the classes. FloodVI was designed at the neighbourhood level and was applied to the Portuguese municipality of Vila Nova de Gaia where several flood events have taken place in the recent past. The FloodVI sensitivity was assessed using three different aggregation methods: the sum of component scores, the first component score and the weighted sum of component scores. The results highlight the sensitivity of the FloodVI to different aggregation methods. Both sum of component scores and weighted sum of component scores have shown similar results. The first component score aggregation method classifies almost all areas as having medium vulnerability and finally the results obtained using the CA show a distinct differentiation of the vulnerability where hot spots can be clearly identified. The information provided by records of previous flood events corroborate the results obtained with CA, because the inundated areas with greater damages are those that are identified as high and very high vulnerability areas by CA. This supports the fact that CA provides a reliable FloodVI.