Prediction of coastal flooding risk under climate change impacts in South Korea using machine learning algorithms

Datos Bibliográficos

ID15544692
Autoressang-jin Park (0000-0003-4240-5682, Seoul National University), Dong-Kun Lee, Dong‐Kun Lee (0000-0001-9969-0117, Seoul National University, autor de correspondencia)
Año2020
Volumen15
Número9
Páginas094052-094052
Fecha de publicación2020-07-14
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaEnvironmental Research Letters (JOURNAL)
Identificadores de la revistaISSN: 1748-9326 • E-ISSN: 1748-9326
EditorialIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/aba5b3
OpenAlexW3042390404
IdiomaEN
Citas recibidas11
Referencias citadas46

Coastal areas have been affected by hazards such as floods and storms due to the impact of climate change. As coastal systems continue to become more socially and environmentally complex, the damage these hazards cause is expected to increase and intensify. To reduce such negative impacts, vulnerable coastal areas and their associated risks must be identified and assessed. In this study, we assessed the flooding risk to coastal areas of South Korea using multiple machine learning algorithms. We predicted coastal areas with high flooding risks, as this aspect has not been adequately addressed in previous studies. We forecasted hazards under different representative concentration pathway climate change scenarios and regional climate models while considering ratios of sea level rise. Based on the results, a risk probability map was developed using a probability ranging from 0 to 1, where higher values of probability indicate areas at higher risk of compound events such as high tides and heavy rainfall. The accuracy of the average receiver operating characteristic curves was 0.946 using a k-Nearest Neighbor algorithm. The predicted risk probability in 10 year increments from the 2030s to the 2080s showed that the risk probability for southern coastal areas is higher than those of the eastern and western coastal areas. From this study, we determined that a probabilistic approach to analyzing the future risk of coastal flooding would be effective to support decision-making for integrated coastal zone management

Climate change · Climatology · Coastal flood · Coastal hazards · Coastal management · Environmental resource management · Flooding (psychology · Geography · Meteorology · Probabilistic logic · Risk assessment · Risk management · Sea level rise · Storm · Coastal and Marine Dynamics · Computer Science · Environmental Science · Flood Risk Assessment and Management · Tropical and Extratropical Cyclones Research · Artificial Intelligence · Geology · Oceanography