Flooding probability of urban area estimated by decision tree and artificial neural networks

Abstract

Remote sensing, such as from satellite, has been recognized as useful for monitoring the changes in hydrology. In this study, we propose a way that is able to estimate flooding probability based on satellite data from the observation network of the World Meteorological Organization. Through a two-stage probability analysis, we can depict the area with high flooding potential in near-real time. In the first stage, decision trees offered a prompt and rough estimation of the flooding probability; in the second stage, artificial neural networks handle the rainfall forecast in a small-scale area. Case studies, simulating two rainfall events on 20 May 2004 and 11 July 2001, proved that our proposed method is promising for mitigating the flooding damage along urban drainage within the downtown area of Kaohsiung city.