NDLI: Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea

Content Provider	Taylor & Francis Online
Author	Jung, Hyung-Sup Lee, Sunmin Lee, Moung Jin Lee, Saro Kim, Jeong-Cheol
Abstract	Since flood frequency increases with the impact of climate change, the damage that is emphasized on flood-risk maps is based on actual flooded area data; therefore, flood-susceptibility maps for the Seoul metropolitan area, for which random-forest and boosted-tree models are used in a geographic information system (GIS) environment, are created for this study. For the flood-susceptibility mapping, flooded-area, topography, geology, soil and land-use datasets were collected and entered into spatial datasets. From the spatial datasets, 12 factors were calculated and extracted as the input data for the models. The flooded area of 2010 was used to train the model, and the flooded area of 2011 was used for the validation. The importance of the factors of the flood-susceptibility maps was calculated and lastly, the maps were validated. As a result, the distance from the river, geology and digital elevation model showed a high importance among the factors. The random-forest model showed validation accuracies of 78.78% and 79.18% for the regression and classification algorithms, respectively, and boosted-tree model showed validation accuracies of 77.55% and 77.26% for the regression and classification algorithms, respectively. The flood-susceptibility maps provide meaningful information for decision-makers regarding the identification of priority areas for flood-mitigation management.
Starting Page	1185
Ending Page	1203
Page Count	19
File Format	PDF HTM / HTML
ISSN	19475705 19475713
DOI	10.1080/19475705.2017.1308971
Journal	Geomatics, Natural Hazards and Risk
Volume Number	8
Issue Number	2
Language	English
Publisher	Taylor & Francis
Publisher Date	2017-04-10
Access Restriction	Open
Subject Keyword	Flood susceptibility Random forest, boosted tree GIS Seoul metropolitan city
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Environmental Science

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