The importance of efficient water resource supply has been acknowledged, and it is essential to predict short-term water consumption in the future. Recently, it has become possible to obtain data on water consumption at the household level through smart water meters. The pattern of these data is nonlinear due to various factors related to human activities, such as holidays and weather. However, it is difficult to accurately predict household water consumption with a nonlinear pattern with the autoregressive integrated moving average (ARIMA) model, a traditional time series prediction model. Thus, this study used a deep learning-based long short-term memory (LSTM) approach to develop a water consumption prediction model for each customer. The proposed model considers several variables to learn nonlinear water consumption patterns. We developed an ARIMA model and an LSTM model in the training dataset for customers with four different water-use types (detached houses, apartment, restaurant, and elementary school). The performances of the two models were evaluated using a test dataset that was not used for model learning. The LSTM model outperformed the ARIMA model in all households (correlation coefficient: mean 89% and root mean square error: mean 5.60 m3). Therefore, it is expected that the proposed model can predict customer-specific water consumption at the household level depending on the type of use.

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Accurate pollutant prediction is essential in fields such as meteorology, meteorological disasters, and climate change studies. In this study, long short-term memory (LSTM) and deep neural network (DNN) models were applied to six pollutants and comprehensive air-quality index (CAI) predictions from 2015 to 2020 in Korea. In addition, we used the network method to find the best data sources that provide factors affecting comprehensive air-quality index behaviors. This study had two steps: (1) predicting the six pollutants, including fine dust (PM10), fine particulate matter (PM2.5), ozone (O3), sulfurous acid gas (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) using the LSTM model; (2) forecasting the CAI using the six predicted pollutants in the first step as predictors of DNNs. The predictive ability of each model for the six pollutants and CAI prediction was evaluated by comparing it with the observed air-quality data. This study showed that combining a DNN model with the network method provided a high predictive power, and this combination could be a remarkable strength in CAI prediction. As the need for disaster management increases, it is anticipated that the LSTM and DNN models with the network method have ample potential to track the dynamics of air pollution behaviors.

Global climate models (GCMs) are used to analyze future climate change. However, the observed data of a specified region may differ significantly from the model since the GCM data are simulated on a global scale. To solve this problem, previous studies have used downscaling methods such as quantile mapping (QM) to correct bias in GCM precipitation. However, this method cannot be considered when certain variables affect the observation data. Therefore, the aim of this study is to propose a novel method that uses a convolution neural network (CNN) considering teleconnection. This new method considers how the global climate phenomena affect the precipitation data of a target area. In addition, various meteorological variables related to precipitation were used as explanatory variables for the CNN model. In this study, QM and the CNN models were applied to calibrate the spatial bias of GCM data for three precipitation stations in Korea (Incheon, Seoul, and Suwon), and the results were compared. According to the results, the QM method effectively corrected the range of precipitation, but the pattern of precipitation was the same at the three stations. Meanwhile, for the CNN model, the range and pattern of precipitation were corrected better than the QM method. The quantitative evaluation selected the optimal downscaling model, and the CNN model had the best performance (correlation coefficient (CC): 69% on average, root mean squared error (RMSE): 117 mm on average). Therefore, the new method suggested in this study is expected to have high utility in forecasting climate change. Finally, as a result of forecasting for future precipitation in 2100 via the CNN model, the average annual rainfall increased by 17% on average compared to the reference data.

To identify the drought and flood control functions of an irrigation reservoir, research on hydrological analysis and its impact needs to be conducted. To this end, geographical characteristics, such as the cross section of the reservoir, are important, but such information is insufficient. Therefore, this study aimed to identify the topographic and morphological characteristics of reservoirs without measured data using their geographical information. In addition, an attempt was made to identify the morphological characteristics of reservoirs that had collapsed due to aging and the increased frequency of occurrence of strong rainfall intensity caused by climate change. Ten reservoirs, including the Ga-Gog Reservoir located in Miryang city, Gyeongsangnam province, South Korea with measured data, were selected as target reservoirs. The topographic information of the target reservoirs was constructed using topographical maps and GIS techniques. Based on the information, the volume (V)-area (A)-depth (H) relationship and the hypsometric curve (HC) according to the relative height (h/H) and relative area (a/A) were created. When the volume of each reservoir estimated using topographic information was compared with the measured volume, the error rate was found be between 0.23 and 14.27%. In addition, two reservoirs that had collapsed near Miryang city were added, and the V-A-H relationship and HCs were created based on the topographic information. In addition, the morphology index, storage-area of full water-levee height relationship, and storage-area of full water relationship were analyzed to identify the morphological characteristics of the reservoirs. The analysis results showed that the collapsed reservoirs had a relatively high water depth and a large area. In addition, similar types of reservoirs were grouped by conducting cluster analysis using basic specifications, such as the reservoir watershed, storage, and area of full water. When the cluster analysis results were analyzed based on HC, the reservoirs were grouped into three shapes: convex upward shape (youthful stage), relatively flat shape (mature stage), and convex downward shape (old stage). The HCs of the collapsed reservoirs exhibited the convex downward shape (old stage), indicating that they were subjected to considerable erosion due to aging. In other words, considerable erosion makes the allowable storage capacity insufficient due to the large amount of sediment accumulated in reservoirs and reduces their flood control capacity, which may cause them to collapse during heavy rainfall. Therefore, it is expected that identifying the potential causes of reservoir collapse through the morphological characteristics and HCs of reservoirs will support the operation and management of reservoirs for reducing flood damage.