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Abstract: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.

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.

Abstract: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.