The transportation network enables movement of people and goods and is the basis of economic activity. Recently, short-term locally heavy rains occur frequently in urban areas, causing serious obstacles to road flooding and increasing economic and social effects. Therefore, in advanced weather countries, many studies have been conducted on realistic and reliable impact forecasting by analyzing socioeconomic impacts, not just information transmission as weather forecasts. In this paper, we use the Spatial Runoff Assessment Tool (S-RAT) and Flood Inundation model (FLO-2D model) to calculate the flooding level in urban areas caused by rainfall and use the flooding rate. In addition, the rainfall–flood depth curve and the Flood–Vehicle Speed curve were presented during the analysis, and the traffic disruption map was prepared using this. The results of this study were compared with previous studies and verified by rainfall events in 2011. As a result of the verification, the result was similar to the actual flooding, and when the same rainfall occurred within the range of the target area, it was confirmed that there were sections that could not be passed and sections that could be passed smoothly. Therefore, the results suggested in this study will be helpful for the driver’s route selection by using the urban flood damage analysis and vehicle driving speed analysis.

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

Recently, human and property damages have often occurred due to various reasons—such as landslides, debris flow, and other sediment-related disasters—which are also caused by regional torrential rain resulting from climate change and reckless development of mountainous areas. Debris flows mainly occur in mountainous areas near urban living communities and often cause direct damages. In general, debris flows containing soil, rock fragments, and driftwood temporarily travel down to lower parts along with a mountain torrent. However, debris flows are also often reported to stream down from the point where a slope failure or a landslide occurs in a mountain directly to its lower parts. The impact of those debris flows is one of the main factors that cause serious damage to structures. To mitigate such damage of debris flows, a quantitative assessment of the impact force is thus required. Moreover, technologies to evaluate disaster prevention facilities and structures at disaster-prone regions are needed. This study developed two models to quantitatively analyze the damages caused by debris flows on structures: Type-1 model for calculating the impact force, which reflected the flow characteristics of debris flows and the Type-2 model, which calculated the impact force based on the topographical characteristics of mountainous regions. Using RAMMS a debris flow runoff model, the impact forces assessed through Type-1 and Type-2 models were compared to check reliability. Using the assessed impact forces, the damage ratio of the structures was calculated and the amount of damage caused by debris flows on the structures was ultimately assessed. The results showed that the Type-1 model overestimated the impact force by 10% and the Type-2 model by 4% for Mt. Umyeon in Seoul, compared to the RAMMS model. In addition, the Type-1 model overestimated the impact force by 3% and Type-2 by 2% for Mt. Majeok in Chuncheon, South Korea.

We propose a technique to measure the impact of loss of traffic function in a particular area to surrounding areas. The proposed method is applied to the c

In recent years, Korea has seen abnormal changes in precipitation and temperature driven by climate change. These changes highlight the increased risks of climate disasters and rainfall damage. Even with weather forecasts providing quantitative rainfall estimates, it is still difficult to estimate the damage caused by rainfall. Damaged by rainfalls differently for inch watershed, but there is a limit to the analysis coherent to the characteristic factors of the inch watershed. It is time-consuming to analyze rainfall and runoff using hydrological models every time it rains. Therefore, in fact, many analyses rely on simple rainfall data, and in coastal basins, hydrological analysis and physical model analysis are often difficult. To address the issue in this study, watershed characteristic factors such as drainage area ($A$), mean drainage elevation ($H$), mean drainage slope ($S$), drainage density ($D$), runoff curve number ($CN$), watershed parameter ($L_p$), and form factor ($R_s$) etc. and hydrologic factors were collected and calculated as independent variables, and the threshold rainfall calculated by the Ministry of Land, Infrastructure and Transport (MOLIT) was calculated as a dependent variable and used in the machine learning technique. As for machine learning techniques, this study uses the support vector machine method (SVM), the random forest method, and eXtreme Gradient Boosting (XGBoost). As a result, XGBoost showed good results in performance evaluation with RMSE 20, MAE 14, and RMSLE 0.28, and the threshold rainfall of the ungauged watersheds was calculated using the XGBoost technique and verified through past rainfall events and damage cases. As a result of the verification, it was confirmed that there were cases of damage in the basin where the threshold rainfall was low. If the application results of this study are used, it is judged that it is possible to accurately predict flooding-induced rainfall by calculating the threshold rainfall in the ungauged watersheds where rainfall-outflow analysis is difficult, and through this result, it is possible to prepare for areas vulnerable to flooding.