@article{ref1,
title="Identifying elements that affect the probability of buildings to suffer flooding in urban areas using Google Street View. A case study from Athens metropolitan area in Greece",
journal="International journal of disaster risk reduction",
year="2017",
author="Diakakis, Michalis and Deligiannakis, Georgios and Pallikarakis, Aggelos and Skordoulis, Michalis",
volume="22",
number="",
pages="1-9",
abstract="Even though numerous methods have been developed to predict the vulnerability of urban areas to flooding, there is still room for improvement in determining the susceptibility of individual buildings. This work aims to identify characteristics that affect a building's probability to suffer flooding and evaluate their influence. The study uses Google Street View to examine 1073 buildings known to have flooded after an extreme precipitation event in 2013 in Athens, Greece and to compare them with characteristics of buildings that did not experience flooding. Using logistic regression, this work investigates the influence of these elements. <br><br>RESULTS show that certain characteristics of buildings increase their probability to flood up to 4.1 times. The study develops an equation involving all influential elements able to predict the buildings that will suffer flooding on a 77% rate.<p /> <p>Language: en</p>",
language="en",
issn="2212-4209",
doi="10.1016/j.ijdrr.2017.02.002",
url="http://dx.doi.org/10.1016/j.ijdrr.2017.02.002"
}