• Journal of Environmental Impact Assessment
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• v.29 no.1
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• pp.61-77
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• 2020

In South Korea, major public waters have been systematic management under national level. Water environment network has been continuous monitoring for change of aquatic ecosystem, river and reservoir. In Water Quality Monitoring Networks, the data have been generally monitored Per eight days or month, while in Automatic Water Quality Monitoring Network the data have been monitored at daily intervals. Therefore, we were compared and analyzed water quality data between the networks using statistic method for same water quality item. Mann-kendall test results confirm that all points in Water Temperature (WT) and DO were not statistically significant. In particular, the result revealed that there is significant variation of TOC in the four different sites, TN in two different sites, TP in three different sites, WT in seven different sites, pH in two different sites between Water Quality Monitoring Network and Automatic Water Quality Monitoring Network. As a result firm LOWESS, TOC and pH clearly shows different trend. Among different sites, the water quality show the significantly positive correlations between at Sinam-Sangju2 and Namgang-Namgang4. Negative correlation significantly appeared in TP (ADD_Lower-AD1 site), TOC (DG-SG site), pH (GR-GR site), TP (JP-CN) and TN, TP, pH, EC, DO (GC-GC2-1 site).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4572-4586
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• 2019

Mobile Edge Computing (MEC) and Information-Centric Networking (ICN) are essential network architectures for the future Internet. The advantages of MEC and ICN such as computation and storage capabilities at the edge of the network, in-network caching and named-data communication paradigm can greatly improve the quality of video streaming applications. However, the packet loss in wireless network environments still affects the video streaming performance and the existing loss recovery approaches in ICN does not exploit the capabilities of MEC. This paper proposes a Deep Learning based Loss Recovery Mechanism (DL-LRM) for video streaming over MEC based ICN. Different with existing approaches, the Forward Error Correction (FEC) packets are generated at the edge of the network, which dramatically reduces the workload of core network and backhaul. By monitoring network states, our proposed DL-LRM controls the FEC request rate by deep reinforcement learning algorithm. Considering the characteristics of video streaming and MEC, in this paper we develop content caching detection and fast retransmission algorithm to effectively utilize resources of MEC. Experimental results demonstrate that the DL-LRM is able to adaptively adjust and control the FEC request rate and achieve better video quality than the existing approaches.

• Journal of Korea Water Resources Association
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• v.34 no.5
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• pp.567-575
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• 2001

A methodology is developed for designing the minimum-cost water distribution network. The method is based on network simulations and an optimization scheme using genetic algorithms. Being a stochastic optimization scheme, genetic algorithms have advantages over the conventional search algorithms in solving network problems known for their nonlinearities and herculean computational costs. While existing methods focus on the design of either entirely new or parallel augmentation of network systems, the proposed method can be applied to problems having both new branches of tree-type and paralle augmentation in loops. The applicability of the method was shown through a case study for Baekryeon water supply system. The optimized design resulted in the maximum 5.37% savings compared to the conventional design without optimization, while meeting the hydraulic constraints.

• Magazine of the Korean Society of Agricultural Engineers
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• v.43 no.4
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• pp.69-78
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• 2001

In this study optimal analysis of pipe network was performed using linear and non linear analysis method for complex real pipe network system of Mungyeong water purification field system which consists of 70 nodes and 86 elements. From the examination result of total flow which is distributed to each pipe, it is found that KYPIPE2 Model supplies less amount than NLAM. It is known that dynamic water level and pressure head of KYPIPE2 Model and NLAM are nearly in accordance with each other from each method of the pipe network analyses, and appeared that both methods of analysis shows high reliable result since the distribution of dynamic water level for every node is the short range of EL. 205.0m~EL. 210.0m besides the pressed dynamic water level. The analysis results of pressure in the methods of pipe network analysis for KYPIPE2 Model and NLAM are similar and it is satisfactory result that the pressure distributions of the tab water design criterion of 5.0kgf/cm$^2$ besides the small part of highland.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.2
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• pp.77-93
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• 1995

A mathematical model for long-rage water supply planning was formulated as a dynamic plant location problem with network arc capacity expansion, and illustative example was presented. The proposed solution procedure identifies economical construction timings of surface water supply facilities and water conveyence systems and the best water supply operating patterns as well. In this study, we present a heuristic solution procedure using Simulated annealing Method in conjunction with Bertsekas & Tseng's RELAXT-II for the 0-1 integer network problem.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1273-1281
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• 1993

Estimation of daily and seasonal evaportranspiration is essential for water resource planning irrigation feasibility study, and real-time irrigation water management . This paper is to evaluate the applicability of neural networks to the estimation of evapotranspiration . A neural network was developed to forecast daily evapotranspiration of the rice crop. It is a three-layer network with input, hidden , and output layers. Back-propagation algorithm with delta learning rule was used to train the neural network. Training neural network wasconducted usign daily actural evapotranspiration of rice crop and daily climatic data such as mean temperature, sunshine hours, solar radiation, relative humidity , and pan evaporation . During the training, neural network parameters were calibrated. The trained network was applied to a set of field data not used in the training . The created response of the neural network was in good agreement with desired values. Evaluating the neural networ performance indicates that neural network may be applied to the estimation of evapotranspiration of the rice crop.

• Journal of Korean Society of Environmental Engineers
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• v.28 no.9
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• pp.961-976
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• 2006

Designing water-reuse network which can reduce the fresh water within the process and increase the water-use efficiency by scientific and systematic analysis is recently interested in the industries. Water systems often allow efficient water uses via water reuse and recirculation in the paper, petrochemical, and steel industries which necessitate a lot of freshwater within the process. Defining network layout connecting water-using process is frequently accomplished by using water pinch technology which optimizes freshwater entering the process and also reduces the wastewater. In this review, recent researches and case studies of water pinch technology which can find the bottleneck of the water stream at the water reuse designing stage are introduced. Necessity of water pinch technology is illustrated by examples of real industries. Recent studies on simultaneous energy and water minimization and water-reuse network among industries in eco-industrial park(EIP) are also introduced.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.2
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• pp.80-89
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• 2014

In this paper, we conceive a context-aware recommendations system for water distribution in future smart water grids, with taking the end users' profiles, water types, network conditions into account. A spectral clustering approach is developed to cluster end users into different communities, based on the end users' common interests in water resources. A back-propagation (BP) neural network is designed to obtain the rating list of the end users' preferences on water resources and the water resource with the highest prediction rating is recommended to the end users. Simulation results demonstrate that the proposed scheme achieves the improved accuracy of recommendation within 2.5% errors notably together with a better user experience in contrast to traditional recommendations approaches.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.146-146
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• 2021

A contamination event occurring in water distribution networks (WDNs) needs to be handled with the appropriate mitigation strategy to protect public health safety and ensure water supply service continuation. Typically the mitigation phase consists of contaminant sensing, public warning, network inspection, and recovery. After the contaminant source has been detected and treated, contaminants still exist in the network, and the contaminated water should be flushed out. The recovery period is critical to remove any lingering contaminant in a rapid and non-detrimental manner. The contaminant flushing can be done in several ways. Conventionally, the opening of hydrants is applied to drain the contaminant out of the system. Relying on advanced information and communication technology (ICT) on WDN management, warning and information can be distributed fast through electronic media. Water utilities can inform their customers to participate in the contaminant flushing by opening and closing their house faucets to drain the contaminated water. The household draining strategy consists of determining sectors and timeslots of the WDN users based on hydraulic simulation. The number of sectors should be controlled to maintain sufficient pressure for faucet draining. The draining timeslot is determined through hydraulic simulation to identify the draining time required for each sector. The effectiveness of the strategy is evaluated using three measurements, such as Wasted Water (WW), Flushing Duration (FD), and Pipe Erosion (PE). The optimal draining strategy (i.e., group and timeslot allocation) in the WDN can be determined by minimizing the measures.

• Journal of Korea Water Resources Association
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• v.44 no.4
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• pp.295-304
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• 2011

For effective water quality management, it is necessary to secure reliable water quality information. There are many variables that need to be included in a comprehensive practical monitoring network : representative sampling locations, suitable sampling frequencies, water quality variable selection, and budgetary and logistical constraints are examples, especially sampling location is considered to be the most important issues. Until now, monitoring network design for water quality management was set according to the qualitative judgments, which is a problem of representativeness. In this paper, we propose network design system for optimal water quality monitoring using the scientific statistical techniques. Network design system is made based on the SAS program of version 9.2 and configured with simple input system and user friendly outputs considering the convenience of users. It applies to Excel data format for ease to use and all data of sampling location is distinguished to sheet base. In this system, time plots, dendrogram, and scatter plots are shown as follows: Time plots of water quality variables are graphed for identifying variables to classify sampling locations significantly. Similarities of sampling locations are calculated using euclidean distances of principal component variables and dimension coordinate of multidimensional scaling method are calculated and dendrogram by clustering analysis is represented and used for users to choose an appropriate number of clusters. Scatter plots of principle component variables are shown for clustering information with sampling locations and representative location.