The interest in renewable energy to replace fossil fuel is increasing as the problem caused by climate change has become more severe. In this study, small hydropower (SHP) was evaluated as a resource with high development value because of its high energy density compared to other renewable energy sources. SHP may be an attractive and sustainable power generation environmental perspective because of its potential to be found in small rivers and streams. The power generation potential could be estimated based on the discharge in the river basin. Since the river discharge depends on the climate conditions, the hydropower generation potential changes sensitively according to climate variability. Therefore, it is necessary to analyze the SHP potential in consideration of future climate change. In this study, the future prospect of SHP potential is simulated for the period of 2021 to 2100 considering the climate change in three hydropower plants of Deoksong, Hanseok, and Socheon stations, Korea. The results show that SHP potential for the near future (2021 to 2040) shows a tendency to be increased, and the highest increase is 23.4% at the Deoksong SPH plant. Through the result of future prospect, we have shown that hydroelectric power generation capacity or SHP potential will be increased in the future. Therefore, we believe that it is necessary to revitalize the development of SHP to expand the use of renewable energy. In addition, a methodology presented in this study could be used for the future prospect of the SHP potential.

In this study, fuzzy mathematics and VIKOR were employed to develop a flood vulnerability assessment system for Ulsan Metropolitan City, South Korea in 2018. HEC-HMS model was used to simulate the major rivers’ runoff in Ulsan Metropolitan City, and HEC-RAS model was used to convert the 1-D runoff simulation results into 2-D results of inundation map, while the simulation results were exhibited through ArcMap. Hazards, sensitivity and adaptivity were selected as the three evaluation indicators for fuzzy comprehensive evaluation, and pressure resilience, state resilience and response resilience were utilized as the indicators for ranking and comparison of the VIKOR method for local governments (administrative districts) of Ulsan Metropolitan City. As the result, Dong-gu district, Ulju-jun County and Nam-gu district showed the best results by both the fuzzy mathematical and the VIKOR method in hazard and pressure, sensitivity and state, adaptivity and response indicator. Fuzzy mathematics and the VIKOR method provide two different ways to study the flood protection capacity of Ulsan Metropolitan City, comparing the historical statistics, VIKOR method’s evaluation results match well with the flooding statistics in Ulsan Metropolitan City.

Instances of flood damage caused by extreme storm rainfall due to climate change and variability have been showing an increasing trend. Particularly, a flood forecasting and warning system has been recognized as an important nonstructural measure for flood damage reduction, including loss of life. Flood forecasting and warning have been performed by the forecasts of flood discharge and flood stage using the physically based rainfall-runoff models. However, recently, studies involving the application of a machine learning-based flood forecasting models, which addresses the limitations of extant physically based flood stage forecasting models, have been performed. We may require various case studies to determine more accurate methods. Therefore, this study performed the real-time forecasting of the river water level or stage at the Gurye station of the Sumjin river with lead times of 1, 3, and 6 h by applying a long short-term memory (LSTM)-based deep learning model. In addition, the applicability of the LSTM model was evaluated by comparing the results with those from widely used models based on support vector machine and multilayer perceptron. Consequently, we noted that the LSTM model exhibited a relatively better forecasting performance. Therefore, the applicability of the LSTM model should be extensively studied for flood forecasting applications.

Many researches are conducted to evaluate benefit from local disaster prevention projects. However, since such evaluation can only be assessed at the local district level, there is a limitation to applying this assessment to the local government's disaster prevention management in that it requires the local characteristics. To compensate for this, in this study, the effect of disaster damage reduction through local governments' local disaster prevention budget was estimated, and that used for disaster damage reduction in the area was calculated. Further, the effect was applied to the damage prediction model that is typical local government-level disaster prevention management, and heavy rain damage prediction was done considering the effect. As a result of applying it to the study area, the effect was prominent in Pyeongtaek-si and Pocheon-si in Gyeonggi-do. Also, considering the results of the effect, it was confirmed that error in the heavy rain damage prediction function was reduced. The results of this study can be used to establish disaster prevention budget management and disaster mitigation plans.

In this study, we proposed a method to quantitatively evaluate the improvement degree of disaster prevention capability through investment of the recovery cost in areas damaged by heavy rain storms. Pyeongtaek, Gwangju, Pocheon, and Hwaseong cities, where heavy rain damage had most frequently occurred during the last 10 years, were selected as the study areas, and damages and recovery cost data for 10 years were collected. The total rainfall and 5-antecedent rainfall during the damage period were used to define the amount of disaster rainfall. A disaster rainfall-damage equation was established to assess disaster prevention capability before and after the investment of the recovery cost. Then, we calculated virtual damage before the investment based on the equation and obtained the damage reduction benefit by the damage difference from before and after the investment. The ratio of recovery cost and damage reduction benefit was assessed to ascertain the improvement degree of disaster prevention capability. From the results, we found that disaster prevention capability can be improved in the range of about 19 to 61 percent by the investment of recovery costs over the past 10 years. Therefore, the results of this study could be used as basic data to quantitatively evaluate the effects of recovery costs and establish an effective disaster management plan.

Smart water grid technologies have been widely adopted as a key component of digital transformation in water resource management, where real-time household water consumption data collected from smart water meters serve as fundamental inputs. However, these datasets often contain numerous outliers and missing values due to communication errors, which degrade data reliability and hinder accurate analysis. This study proposes an improved framework for outlier detection and missing data imputation tailored to the characteristics of cumulative household water consumption data. The proposed imputation methods were evaluated against conventional approaches using error metrics, and the results demonstrated significant improvements in accuracy, with RMSE values substantially lower than those of the reference method. In addition, prediction models with varying levels of complexity were explored to examine how improved data quality influences forecasting performance. The results indicate that, although data preprocessing enhances data reliability, prediction performance remains limited due to the inherent variability and stochastic nature of household water consumption data. Prediction models with varying levels of complexity were constructed and evaluated using the corrected datasets. The performance of the models varied depending on dataset characteristics, and no single model consistently outperformed others. Overall, this study highlights the critical role of data quality improvement in smart water management systems and provides practical insights into missing data imputation, while suggesting that further advancements in prediction require additional explanatory variables and more sophisticated modeling approaches.