Flood Risk Assessment in Urban Areas Based on Spatial Analytics and Social Factors

Abstract

Flood maps alone are not sufficient to determine and assess the risks to people, property, infrastructure, and services due to a flood event. Simply put, the risk is almost zero to minimum if the flooded region is "empty" (i.e., unpopulated, has not properties, no industry, no infrastructure, and no socio-economic activity). High spatial resolution Earth Observation (EO) data can contribute to the generation and updating of flood risk maps based on several aspects including population, economic development, and critical infrastructure, which can enhance a city's flood mitigation and preparedness planning. In this case study for the Don River watershed, Toronto, the flood risk is determined and flood risk index maps are generated by implementing a methodology for estimating risk based on the geographic coverage of the flood hazard, vulnerability of people, and the exposure of large building structures to flood water. Specifically, the spatial flood risk index maps have been generated through analytical spatial modeling which takes into account the areas in which a flood hazard is expected to occur, the terrain's morphological characteristics, socio-economic parameters based on demographic data, and the density of large building complexes. Generated flood risk maps are verified through visual inspection with 3D city flood maps. Findings illustrate that areas of higher flood risk coincide with areas of high flood hazard and social and building exposure vulnerability.

Flood Risk Mapping Using GIS and Multi-Criteria Analysis: A Greater Toronto Area Case Study

“…Another study by Abdalla [19] used a web-based three-dimensional (3D) GIS approach for flood risk assessment, in which the Only limited studies of flood risk assessment (which include hazard, social, and economic vulnerability) in the GTA have been developed. A previous study [18] (which serves as the basis of the present research) generated flood risk index maps using GIS through the spatial combination of the flood hazard, social, and economic vulnerability layers. The criteria used to generate the flood hazard map were slope, land depression, the floodplain map (provided by the Toronto and Region Conservation Authority, TRCA), and land cover.…”

“…In the present paper, several improvements of the previous study for the GTA [18] are proposed. First, we implement MCDM as part of the methodology for determining the criteria weights.…”

“…The floodplain for the Don River watershed was generated and provided by the TRCA using hydrological and hydraulic models. As this map only contains the area (boundary) of the floodplain, the reclassification consisted of assigning a value of 5 to all of the cells that are within the boundary, and a value of 2 (following the selection in [18]) to those that are outside the boundary.…”

“…The physical exposure refers to man-made infrastructure elements that are exposed to damages due to flooding [18]. Generally, these physical damages are measured in terms of economic losses.…”

Leveraging machine learning for predicting flash flood damage in the Southeast US

“…Generally, risk refers to the potential losses of a particular hazard (Cardona et al 2012, Armenakis et al 2017, which is characterized as a function of three major components: hazard, vulnerability, and exposure (Adger 2006, Dang et al 2010, Winsemius et al 2013, Budiyono et al 2015, Koks et al 2015. Assessing flash flood risk components has been the subject of several studies.…”