A Spatial Analysis Approach for Urban Flood Occurrence and Flood Impact Based on Geomorphological, Meteorological, and Hydrological Factors

Elissavet Feloni (), Andreas Anayiotos and Evangelos Baltas
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Elissavet Feloni: Department of Mechanical Engineering and Materials Science and Engineering, Cyprus University of Technology, 45 Kitiou Kyprianou Str., Dorothea Building, 5th Floor, 3041 Limassol, Cyprus
Andreas Anayiotos: Department of Mechanical Engineering and Materials Science and Engineering, Cyprus University of Technology, 45 Kitiou Kyprianou Str., Dorothea Building, 5th Floor, 3041 Limassol, Cyprus
Evangelos Baltas: Department of Water Resources and Environmental Engineering, School of Civil Engineering, National Technical University of Athens, 5 Iroon Polytechniou, 15780 Athens, Greece

Geographies, 2022, vol. 2, issue 3, 1-12

Abstract: Urban flooding can cause significant infrastructure and property damage to cities, loss of human life, disruption of human activities, and other problems and negative consequences on people and the local government administration. The objective of this research work is to investigate the relation between urban flood occurrence and potentially flood-triggering factors. The analysis is performed in the western part of Athens Basin (Attica, Greece), where over the past decades several flood events caused human losses and damages to properties and infrastructure. Flood impact is measured by the number of citizen calls for help to the emergency line of the fire service, while potentially influencing factors are several geomorphological characteristics of the area and hydrometeorological indices regarding storms, which were determined with the aid of GIS techniques. The analysis is based on the investigation on binary logistic regression and generalized linear regression models that are used to build relationships between the potentially flood-influencing factors and the flood occurrence/impact for three events that were selected for reasons of comparison. The entire analysis highlights the variations attributed to the consideration of different factors, events, as well as to the different cell size of the grid used in the analysis. Results indicate that, the binary logistic regression model performed for flood occurrence achieves higher predictability, compared to the ability of the model used to describe flood impact.

Keywords: urban flooding; flood impact; flood-influencing factors; spatial analysis; floods; Athens (search for similar items in EconPapers)
JEL-codes: Q1 Q15 Q5 Q53 Q54 Q56 Q57 (search for similar items in EconPapers)
Date: 2022
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