• Water for future
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• v.51 no.12
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• pp.63-67
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• 2018

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.6
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• pp.133-141
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• 2009

The main objective of water distribution system is to supply enough water to users with proper pressure. Hydraulic analysis of water distribution system can be divided into Demand Driven Analysis (DDA) and Pressure Driven Analysis (PDA). Demand-driven analysis can give unrealistic results such as negative pressures in nodes due to the assumption that nodal demands are always satisfied. Pressure-driven analysis which is often used as an alternative requires a Head-Outflow Relationship (HOR) to estimate the amount of possible water supply at a certain level of pressure. However, the lack of data causes difficulty to develop the relationship. In this study, effective supply, which is the possible amount of supply while meeting the pressure requirement in nodes, is proposed to estimate the serviceability and user's convenience of the network. The effective supply is used to calculate Subsystem Importance Index (SII) which indicates the effect of isolating a subsystem on the entire network. Harmony Search, a stochastic search algorithm, is linked with EPANET to maximize the effective supply. The proposed approach is applied in example networks to evaluate the capability of the network when a subsystem is isolated, which can also be utilized to prioritize the rehabilitation order or evaluate reliability of the network.

• Journal of Korea Water Resources Association
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• v.56 no.7
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• pp.439-449
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• 2023

Recent advancements in data measuring technology have facilitated the installation of various sensors, such as pressure meters and flow meters, to effectively assess the real-time conditions of water distribution systems (WDSs). However, as cities expand extensively, the factors that impact the reliability of measurements have become increasingly diverse. In particular, demand data, one of the most significant hydraulic variable in WDS, is challenging to be measured directly and is prone to missing values, making the development of accurate data generation models more important. Therefore, this paper proposes an adversarially trained autoencoder (ATAE) model based on generative deep learning techniques to accurately estimate demand data in WDSs. The proposed model utilizes two neural networks: a generative network and a discriminative network. The generative network generates demand data using the information provided from the measured pressure data, while the discriminative network evaluates the generated demand outputs and provides feedback to the generator to learn the distinctive features of the data. To validate its performance, the ATAE model is applied to a real distribution system in Austin, Texas, USA. The study analyzes the impact of data uncertainty by calculating the accuracy of ATAE's prediction results for varying levels of uncertainty in the demand and the pressure time series data. Additionally, the model's performance is evaluated by comparing the results for different data collection periods (low, average, and high demand hours) to assess its ability to generate demand data based on water consumption levels.

• Water for future
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• v.28 no.2
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• pp.169-180
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• 1995

The cost of a looped pipe network is affected by a set of loop flows. The mathematical model for optimizing the looped pipe network is expressed in the optimal set of loop flows to apply to a stochastic optimization method. Because the feasible region of the looped pipe network problem is nonconvex with multiple local optima, the Modified Stochastic Probing Method is suggested to efficiently search the feasible region. The method consists of two phase: i) a global search phase(the stochastic probing method) and ii) a local search phase(the nearest neighbor method). While the global search sequentially improves a local minimum, the local search escapes out of a local minimum trapped in the global search phase and also refines a final solution. In order to test the method, a standard test problem from the literature is considered for the optimal design of the paralled expansion of an existing network. The optimal solutions thus found have significantly smaller costs than the ones reported previously by other researchers.

• Environmental Engineering Research
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• v.21 no.4
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• pp.333-340
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• 2016

An Artificial Neural Network including a Radial Basis Function (RBF) and a Time Delay Neural Network (TDNN) was used to predict total dissolved solid (TDS) in the river Zayanderud. Water quality parameters in the river for ten years, 2001-2010, were prepared from data monitored by the Isfahan Regional Water Authority. A factor analysis was applied to select the inputs of water quality parameters, which obtained total hardness, bicarbonate, chloride and calcium. Input data to the neural networks were pH, $Na^+$, $Mg^{2+}$, Carbonate ($CO{_3}^{-2}$), $HCO{_3}^{-1}$, $Cl^-$, $Ca^{2+}$ and Total hardness. For learning process 5-fold cross validation were applied. In the best situation, the TDNN contained 2 hidden layers of 15 neurons in each of the layers and the RBF had one hidden layer with 100 neurons. The Mean Squared Error and the Mean Bias Error for the TDNN during the training process were 0.0006 and 0.0603 and for the RBF neural network the mentioned errors were 0.0001 and 0.0006, respectively. In the RBF, the coefficient of determination ($R^2$) and the index of agreement (IA) between the observed data and predicted data were 0.997 and 0.999, respectively. In the TDNN, the $R^2$ and the IA between the actual and predicted data were 0.957 and 0.985, respectively. The results of sensitivity illustrated that $Ca^{2+}$ and $SO{_4}^{2-}$ parameters had the highest effect on the TDS prediction.

• Journal of Environmental Science International
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• v.18 no.9
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• pp.961-970
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• 2009

This paper has attempted to improve the quality of urban environment in terms of the management of urban green tract and suggest a way of coexistence between human and nature by proposing a plan to establish green network using an urban green zone based on 'linear concept' instead of point and plane concepts. The results have turned out as follows: 1. According to current status of forest functions, forest recreation area has reached 39.6%, satisfying citizens' needs. However, the space for living environment is just about 20% with a lack of a green zone. Therefore, it's been necessary to establish green network using roadside trees and take advantage of them as sustainable living space along with existing green tract. 2. With forest in the suburbs and Geumhogang which is the tributary to the Nakdonggang, Sincheon (stream) flows through the downtown. It connects mountains including Waryongsan from the south to the north around Duryu Park and Dalseong Park. Therefore, the water system that passes through Palgongsan (Mt.) and Biseulsan (Mt.) would make it possible to connect with the parks in the downtown. 3. According to this paper, it appears that it's necessary to establish green network through roof or wall greening by focusing on the existing green tract in the urban parks and suburbs and taking advantage of roadside trees and water system.

• Korean Journal of Ecology and Environment
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• v.37 no.4 s.109
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• pp.373-377
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• 2004

Since, in 1970, diatoms and diatom indices was first used in measuring quality of streams and rivers at the Seine Water Agency in France, five other water agencies began to show interests since 1990. In 1994, associated with CEMAGREF (Centre National du Machinisme Agricole du Genie Rural et des Eaux et des Forets : environmental science and expertise for the sustainable management of land and water), the six French Water Agencies (Seine, Rhone-Mediterranee-Corse, Artois- Picardie, Loire-Bretagne, Rhin-Meuse and Adour-Garonne) developed a practical diatom index, which is liable to be used routinely in the territorial streams and rivers of whole France, and which is liable to promote and facilitate its use in monitoring water networks. In 1995, the first version of a biological diatom index (IBD) was generated by them. Since then, the software update for IBD calculation and the user's network have led to numerous practical applications in France. Furthermore, the Water Agencies have run applicable programs on the National Basin Network from 1996, and the initial data set of IBD was completed. Re- examination of the complete data set was done at the end of 1998, and the tests on different calculation options of the IBD led to a third version of this index in June,2000 (AFNOR NF T 90-354).

• Journal of Korean Society on Water Environment
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• v.30 no.2
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• pp.138-147
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• 2014

In order to determine the best operating rules for the Nakdong River, three cases were applied to analyze the simulated results of water supply capacity by HEC-ResSim model. This study discussed to present the best operating rules for conjunctive operating of existing the dams and new constructed the weirs through system network. The system network was constructed considering the water supply, the river environment and the operating facility. The water supply capacities are separately evaluated for each case applying the best rules. It is case1 that the dams are operated individually, case2 that the dams are operated in conjunction with the others dams, and case3 that dams and weirs are operated in conjunction with the others dams-weirs. Comparing the cases, case 3 has shown the best water supply capacity of the Nakdong River.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.198-203
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• 2020

In this paper, we propose an artificial water level prediction system for small river flood prediction. River level prediction can be a measure to reduce flood damage. However, it is difficult to build a flood model in river because of the inherent nature of the river or rainfall that affects river flooding. In general, the downstream water level is affected by the water level at adjacent upstream. Therefore, in this study, we constructed an artificial intelligence model using Recurrent Neural Network(LSTM) that predicts the water level of downstream with the water level of two upstream points. The proposed artificial intelligence system designed a water level meter and built a server using Nodejs. The proposed neural network hardware system can predict the water level every 6 hours in the real river.

• Computers and Concrete
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• v.13 no.6
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• pp.739-748
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• 2014

In this article, by using experimental studies and artificial neural network has been tried to investigate the use of nano-silica as concrete admixture to reduce alkali-silica reaction. If there are reactive aggregates and alkali of cement with enough moisture in concrete, a gel will be formed. Then with high reactivity between alkali of cement and existence of silica in aggregates, this gel will expand by absorption of water, and causes expansive pressure and cracks be formed. At the time passes, this gel will reduce both durability and strength of the concrete. By reducing the size of silicate to nano, specific surface area of particles and number of atoms on the surface will be increased, which causes more pozzolanic activity of them. Nano-silica can react with calcium hydroxide ($Ca(OH)_2$) and produces C-S-H gel. In this study, accelerated mortar bar specimens according to ASTM C 1260 and ASTM C 1567, with different mix proportions were prepared using aggregates of Kerman, such as: none admixture and plasticizer, different proportions of nano-silica separately. By opening the moulds after 24 hour and curing in water at $80^{\circ}C$ for 24 hour, then curing in (1N NaOH) at $80^{\circ}C$ for 14 days, length expansion of mortar bars were measured and compared. It was noted that, the lowest length expansion of a specimens shows the best proportion of admixture based on alkali-silica reactivity. Then, prediction of alkali-silica reaction of concrete has been investigated by using artificial neural network. In this study the backpropagation network has been used and compared with different algorithms to train network. Finally, the best amount of nano silica for adding to mix proportion, also the best algorithm and number of neurons in hidden layer of artificial neural network have been offered.