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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.

Energy consumption in water supply systems is closely connected with the demand for water, since energy is mostly consumed in the process of water transport and distribution, in addition to the energy that might be needed to pump the water from its sources. Existing studies have been carried out on optimizing the pump operations to attain appropriate pressure and on controlling the water level of storage facilities to transfer the required demand and to reduce the energy cost. The idea is to reduce the amount of the water being supplied when the unit price of energy is high and to increase the supply when the unit price is low. To realize this scheme, the energy consumption of water supply systems, the amount of water transfer, the organization of energy cost structure, the utilization of water tanks, and so forth are investigated and analyzed to establish a model of optimized water demand management based on the application of water tanks in supplied areas. In this study, with the assumption that energy cost can be reduced by the redistribution of a demand pattern, a numerical analysis is conducted on transferring water demand at storage facilities from the peak energy cost hours to the lower energy cost hours. This study was applied at the Bupyeong 2 reservoir catchment, Incheon, Korea.

This limited review of smart water grid (SWG) development, challenges, and solutions provides an initial assessment of early attempts at operating SWGs. Though the cost and adoption issues are critical, potential benefits of SWGs such as efficient water conservation and distribution sustain the development of SWGs around the world. The review finds that the keys to success are the new regulations concerning data access and ownership to solve problems of security and privacy; consumer literacy to accept and use SWGs; active private sector involvement to coordinate SWG development; government-funded pilot projects and trial centers; and integration with sustainable water management.

Abstract-We carried out a literature review to find evidence from empirical studies that constructed wetlands (CWs) can increase biodiversity at the site or landscape level. A set of criteria from general and theoretical ecology was developed that we found useful for defining 'best practice' in the construction of wetlands (e.g. landscape connectivity, area versus size, disturbance regime). Thereafter, we analyzed 21 original research papers where biodiversity development after wetland construction was documented. Wetland construction is a established routine procedure serving various purposes in environmental protection, such as waste water retention and treatment. 'Best practice' criteria with respect to biodiversity protection were not regularly applied during the construction and monitoring process. The published records were substantially different as far as methodological approaches and aims are concerned. They contained short-term snapshot studies to long-term monitoring of biotic and abiotic conditions. Only a few case studies were published in international journals where biodiversity improvement in terms of specific biodiversity indicators was well-documented. A general conclusion whether or not biodiversity is enhanced by CWs cannot be drawn from the published record. As there are confirming results in some studies, we conclude that under certain circumstances constructed wetlands can be useful complements to other biodiversity conservation strategies.

Many problems that are encountered in regards to water balance and resources management are related to challenges of economic development under limited resources and tough competition among various water uses. The development of major infrastructure like airports in remote areas that have limited water resources is becoming a common problem. In order to overcome these difficulties, water management has to articulate and combine several resources in order to respond to various demands while preserving the ecological quality of the environment. The paper discusses the interest in implementing the Smart Water Grid concept on Yeongjongdo Island, which is the location of Korea's main airport. This new concept is based on the connection of various water resources and their optimized management with new information technology solutions. The proposed system integrates water generated through rainfall, external water resources (i.e., metropolitan water distribution system), gray water and other types of alternative water resources. The paper analyses the feasibility of this approach and explores interest in the Smart Water Grid concept.

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.