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

Abstract

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.