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

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 inf...

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Bibliographic Details
Published in:Geomatics, natural hazards and risk natural hazards and risk, 2017-12, Vol.8 (2), p.1185-1203
Main Authors: Lee, Sunmin, Kim, Jeong-Cheol, Jung, Hyung-Sup, Lee, Moung Jin, Lee, Saro
Format: Article
Language:English
Subjects:
Algorithms
Area
Classification
Climate change
Datasets
Digital Elevation Models
Environmental impact
Environmental risk
Flood damage
Flood frequency
Flood management
Flood mapping
Flood predictions
Flood risk
Flood susceptibility
Floods
Frequency analysis
Geographic information systems
Geographical information systems
Geology
GIS
Hydrologic data
Impact damage
Information systems
Land use
Metropolitan areas
Mitigation
Model accuracy
random forest, boosted tree
Remote sensing
River discharge
Rivers
Satellite navigation systems
Seoul metropolitan city
Soil
Soil mapping
Topography (geology)
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Summary: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.
ISSN:1947-5705
1947-5713
DOI:10.1080/19475705.2017.1308971