Climate polarization due to global warming has increased the intensity of drought in some regions, and the need for drought estimation studies to help minimize damage is increasing. In this study, we constructed remote sensing and climate data for Boryeong, Chungcheongnam-do, Korea, and developed a model for drought index estimation by classifying data characteristics and applying multiple linear regression analysis. The drought indices estimated in this study include four types of standardized precipitation indices (SPI1, SPI3, SPI6, and SPI9) used as meteorological drought indices and calculated through cumulative precipitation. We then applied statistical analysis to the developed model and assessed its ability as a drought index estimation tool using remote sensing data. Our results showed that its adj.R² value, achieved using cumulative precipitation for one month, was very low (approximately 0.003), while for the SPI3, SPI6, and SPI9 models, the adj.R² values were significantly higher than the other models at 0.67, 0.64, and 0.56, respectively, when the same data were used.

Urban drainage systems (UDSs) continue to face challenges, despite numerous efforts to improve their sustainability through design, planning, and management. The goal of such initiatives is to avoid and minimize flooding as well as maintain the UDS’s sustainable functionality, which can be analyzed using a stormwater management model (SWMM). In this study, a multiobjective automatic parameter-calibration (MAPC) framework was developed based on the SWMM. It consisted of three steps: sensitivity analysis (Step I), objective selection (Step II), and SWMM parameter calibration (Step III). The proposed MAPC framework was verified using the Yongdap drainage network located in Seoul, South Korea. The resultant MAPC framework demonstrated that the system characteristics (such as percent of impervious area and hillslope) and problems in UDS design, planning, and management can be well reflected by the corresponding model. The MAPC framework proposed in this study can contribute to UDS modeling sustainability.

Drought generally affects a wider area for longer period of time than other natural disasters. Recently, so-called "mega-droughts" of very long durations and with more extensive impacts have been observed across the world. However, few efforts have been devoted to identifying the critical impact factors-such as climate, hydrological and socioeconomic factors-for such extreme droughts. In this study, we identify the most critical factor and derive the direct/indirect relationship among nine influences including climatic, hydrological, and socioeconomic factors using two feature selection methods. The two feature selection approaches we use are based on the statistical theories of principal component analysis and linear discriminant analysis. The standardized precipitation index (SPI) is used as a drought index. The study area is Boryeong city, South Korea. Drought impact analysis results obtained from the two feature selection approaches are compared and presented by polygon radar charts. The results showed that the low water level of the Boryeong dam is the most critical impact factor.

Recently, flood damages have increased due to torrential rains driven by climate changes. In order to overcome the limitation of traditional manpower-dependent river monitoring approaches (low level of automation and system rapidity), CCTV-based digitalized monitoring techniques should be developed for promptly monitoring, predicting, and responding to flood damages. This study introduces an integrated AI-CCTV model for both outland and inland flood monitoring in real-time: Optical-Flow-based Flow Estimation Model (OF2EM) for outland river discharge estimation and Multi-Object-based Segmentation for Flood Depth Estimation Model (MOSDEM) for inland flood inundation. The two sub-modules of the integrated model were demonstrated independently to each domain, (1) the former OF2EM in a laboratory flume with various hydraulic and optical conditions (e.g., different brightness contrast, optical interference, with hydraulic structure that generates eddy flows) and (2) the latter MOSDEM to identify each different group of objects in a CCTV frame and finally to quantify the extent and depth of inundated area. Finally, the proposed integrated AI-CCTV model is verified to confirm the benefit of integrating the dual sub-models of real-time flood monitoring (e.g., inundated water propagation prediction with OF2EM in one corner of a frame and inundation boundary and severity determination with MOSDEM in another corner of the frame).

Recently, detecting flooded areas in CCTV images was performed based on semantic segmentation models (e.g., U-Net, FCN, etc.). However, these flooded area detection techniques are based on large-scale manually annotated images, which consume manpower and time. Image augmentation is one of the ways to overcome the limitations mentioned above. Some previous studies have used image augmentation to improve the performance of flooded area detection by combining two or more methods. However, there has been no study quantifying which augmentation methods are reasonable. This study aims to verify which image augmentation method is reasonable to improve the performance of urban flooded area detection techniques. First, this study develops a flood area detection technology corresponding to training images augmented with five different methods (Brightness, Blur, Contrast, Rotation, Crop). Subsequently, the performance changes for each technique were quantified, and characteristics related to the performance variations of each method were examined.