Leaks in a water distribution network (WDS) constitute losses of water supply caused by pipeline failure, operational loss, and physical factors. This has raised the need for studies on the factors affecting the leakage ratio and estimation of leakage volume in a water supply system. In this study, principal component analysis (PCA) and artificial neural network (ANN) were used to estimate the volume of water leakage in a WDS. For the study, six main effective parameters were selected and standardized data obtained through the Z-score method. The PCA-ANN model was devised and the leakage ratio was estimated. An accuracy assessment was performed to compare the measured leakage ratio to that of the simulated model. The results showed that the PCA-ANN method was more accurate for estimating the leakage ratio than a single ANN simulation. In addition, the estimation results differed according to the number of neurons in the ANN model’s hidden layers. In this study, an ANN with multiple hidden layers was found to be the best method for estimating the leakage ratio with 12–12 neurons. This suggested approaches to improve the accuracy of leakage ratio estimation, as well as a scientific approach toward the sustainable management of water distribution systems.

Diverse drought indices have been developed and used across the globe to assess and monitor droughts. Among them, the Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) are drought indices that have been recently developed and are being used in the world’s leading countries. This study took place in Korea’s major observatories for drought prediction until 2100, using the Representative Concentration Pathway (RCP) 8.5 scenario. On the basis of the drought index measured by SPI, future climates were forecast to be humid, as the index would rise over time. In contrast, the RDI, which takes evapotranspiration into account, anticipated dry climates, with the drought index gradually falling over time. From the analysis of the drought index through the RCP 8.5 scenario, extreme drought intensity will be more likely to occur due to rising temperatures. To obtain the diversity of drought prediction, the evapotranspiration was deemed necessary for calculating meteorological droughts.

The non-revenue water (NRW) ratio in a water distribution system is the ratio of the loss due to unbilled authorized consumption, apparent losses and real losses to the overall system input volume (SIV). The method of estimating the NRW ratio by measurement might not work in an area with no district metered areas (DMAs) or with unclear administrative district. Through multiple regression analyses is a statistical analysis method for calculating the NRW ratio using the main parameters of the water distribution system, although its disadvantage is lower accuracy than that of the measured NRW ratio. In this study, an artificial neural network (ANN) was used to estimate the NRW ratio. The results of the study proved that the accuracy of NRW ratio calculated by the ANN model was higher than by multiple regression analysis. The developed ANN model was shown to have an accuracy that varies depending on the number of neurons in the hidden layer. Therefore, when using the ANN model, the optimal number of neurons must be determined. In addition, the accuracy of the outlier removal condition was higher than that of the original data used condition.

Drought is a meteorological disaster that has serious economic and environmental impacts. Therefore, for drought assessment, it is important to not only analyze the current state of drought using observed data but also future droughts by considering climate change. Although shared socioeconomic pathways (SSPs) have been developed, studies regarding their application in drought analysis in Korea are insufficient. In this study, the standardized precipitation index (SPI) and reconnaissance drought index (RDI) for future droughts in Korea were calculated and analyzed using climate data based on SSP scenarios. The precipitation and temperature data from SSP1-2.6 and SSP5-8.5 scenarios were analyzed. In both scenarios, temperature and precipitation increased toward the long-term future, with the increase being more in the SSP5-8.5 scenario than in the SSP1-2.6 scenario. Using these data, the number of drought occurrences was found to be smaller, and the two drought indices were found to be lower in SSP5-8.5 scenario than in the SSP1-2.6 scenario, implying that more severe droughts may occur in the former than in the latter. In addition, in SSP1-2.6 scenarios, both SPI and RDI increased toward the long-term future, while in SSP5-8.5 scenario, SPI increased, and RDI decreased toward the long-term future.

The non-revenue water (NRW) ratio in water distribution networks is the ratio of losses from unbilled authorized consumption and apparent and real losses to the total water supply. NRW is an important parameter for prioritizing the improvement of a water distribution system and identifying the influencing parameters. Though the method using multiple regression analysis (MRA) is a statistical analysis method for estimating the NRW ratio using the main parameters of a water distribution system, it has disadvantages in that the accuracy is low compared to the measured NRW ratio. In this study, an artificial neural network (ANN) was applied to estimate the NRW ratio to improve assessment accuracy and suggest an efficient methodology to identify related parameters of the NRW ratio. When using an ANN with the optimal number of neurons, the accuracy of estimation was higher than that of conventional statistical methods, as with MRA.

Nonrevenue water (NRW) in a water distribution network is the water lost from unbilled authorized consumption, apparent losses, and real losses compared to the total system input volume. Nonrevenue water is an important parameter for prioritizing water distribution network improvement intervention planning, and it is necessary to identify the affecting parameters. A factor classification system has been developed based on the factors suggested by major institutions and researchers to propose an effective NRW classification system in a water distribution network. The factor classifications used include physical, operational, and socioeconomic factors that could affect NRW. Appropriate standards are required when classifying water main parameters. In this study, three criteria were proposed to create independent factors. The first relates to the properties of the parameter. One determines whether the parameters related to the water network are more suitable for physical, operational, or socioeconomic factors and classifies them into one of these three parameters. Second, one considers data availability and data characteristics taking into account the scope of the coverage area. Third, it must be possible to quantify selected parameter data. Whether the collected data are numerically valid and whether it can be used as a standard for assessment or comparison between regions must be examined. The quantification portion of the qualitative data in managing NRW is important and needs to be used in accordance with reasonable standards. In this study, more factors can be used depending on those selected, and it was found that NRW prediction that reflects regional characteristics is possible.