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Article

Spatial Assessment of Urban Flood Susceptibility Using Data Mining and Geographic Information System (GIS) Tools

by 1,2, 3,4, 1,* and 2,*
1
Center for Environmental Assessment Monitoring, Environmental Assessment Group, Korea Environment Institute (KEI), 370 Sicheong-Daero, Sejong 30147, Korea
2
Department of Geoinformatics, University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul 02504, Korea
3
Geological Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124 Gwahak-ro, Yuseong-gu, Daejeon 34132, Korea
4
Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro, Yuseong-gu, Daejeon 34113, Korea
*
Authors to whom correspondence should be addressed.
Sustainability 2018, 10(3), 648; https://doi.org/10.3390/su10030648
Received: 2 January 2018 / Revised: 12 February 2018 / Accepted: 15 February 2018 / Published: 28 February 2018
(This article belongs to the Special Issue Floods and Landslides: A Sustainability Approach)
Using geographic information system (GIS) tools and data-mining models, this study analyzed the relationships between flood areas and correlated hydrological factors to map the regional flood susceptibility of the Seoul metropolitan area in South Korea. We created a spatial database of data describing factors including topography, geology, soil, and land use. We used 2010 flood data for training and 2011 data for model validation. Frequency ratio (FR) and logistic regression (LR) models were applied to 2010 flood data to determine the relationships between the flooded area and its causal factors and to derive flood-susceptibility maps, which were substantiated using the area flooded in 2011 (not used for training). As a result of the accuracy validation, FR and LR model results were shown to have 79.61% and 79.05% accuracy, respectively. In terms of sustainability, floods affect water health as well as causing economic and social damage. These maps will provide useful information to decision makers for the implementation of flood-mitigation policies in priority areas in urban sustainable development and for flood prevention and management. In addition to this study, further analysis including data on economic and social activities, proximity to nature, and data on population and building density, will make it possible to improve sustainability. View Full-Text
Keywords: flood susceptibility; data mining; spatial database; geographic information system (GIS) flood susceptibility; data mining; spatial database; geographic information system (GIS)
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MDPI and ACS Style

Lee, S.; Lee, S.; Lee, M.-J.; Jung, H.-S. Spatial Assessment of Urban Flood Susceptibility Using Data Mining and Geographic Information System (GIS) Tools. Sustainability 2018, 10, 648. https://doi.org/10.3390/su10030648

AMA Style

Lee S, Lee S, Lee M-J, Jung H-S. Spatial Assessment of Urban Flood Susceptibility Using Data Mining and Geographic Information System (GIS) Tools. Sustainability. 2018; 10(3):648. https://doi.org/10.3390/su10030648

Chicago/Turabian Style

Lee, Sunmin, Saro Lee, Moung-Jin Lee, and Hyung-Sup Jung. 2018. "Spatial Assessment of Urban Flood Susceptibility Using Data Mining and Geographic Information System (GIS) Tools" Sustainability 10, no. 3: 648. https://doi.org/10.3390/su10030648

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