• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.939-953
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• 2023

Shallow water equations (SWE) serve as fundamental equations governing the movement of the water. Traditional numerical approaches for solving these equations generally face various challenges, such as sensitivity to mesh generation, and numerical oscillation, or become more computationally unstable around shock and discontinuities regions. In this study, we present a novel approach that leverages the power of physics-informed neural networks (PINNs) to approximate the solution of the SWE. PINNs integrate physical law directly into the neural network architecture, enabling the accurate approximation of solutions to the SWE. We provide a comprehensive methodology for formulating the SWE within the PINNs framework, encompassing network architecture, training strategy, and data generation techniques. Through the results obtained from experiments, we found that PINNs could be an accurate output solution of SWE when its results were compared with the analytical method. In addition, PINNs also present better performance over the Artificial Neural Network. This study highlights the transformative potential of PINNs in revolutionizing water resources research, offering a new paradigm for accurate and efficient solutions to the SVE.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.199-207
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• 2024

Local reservoirs are crucial sources for agricultural water supply, necessitating stable water level management to prepare for extreme climate conditions such as droughts. Water level prediction is significantly influenced by local climate characteristics, such as localized rainfall, as well as seasonal factors including cropping times, making it essential to understand the correlation between input and output data as much as selecting an appropriate prediction model. In this study, extensive multivariate data from over 400 reservoirs in Jeollabuk-do from 1991 to 2022 was utilized to train and validate a water level prediction model that comprehensively reflects the complex hydrological and climatological environmental factors of each reservoir, and to analyze the impact of each input feature on the prediction performance of water levels. Instead of focusing on improvements in water level performance through neural network structures, the study adopts a basic Feedforward Neural Network composed of fully connected layers, batch normalization, dropout, and activation functions, focusing on the correlation between multivariate input data and prediction performance. Additionally, most existing studies only present short-term prediction performance on a daily basis, which is not suitable for practical environments that require medium to long-term predictions, such as 10 days or a month. Therefore, this study measured the water level prediction performance up to one month ahead through a recursive method that uses daily prediction values as the next input. The experiment identified performance changes according to the prediction period and analyzed the impact of each input feature on the overall performance based on an Ablation study.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.10
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• pp.1475-1480
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• 2013

In this paper, the basic design study of a water-cooling jacket, which have reported no cases for applying to railway traction motors so far, were conducted for applying to Interior Permanent Magnet Synchronous Motor (IPMSM) for railway vehicles. The basic thermal characteristics analysis of the 110kW-class IPMSM was performed by using 3-dimentional thermal equivalent network method. The necessary design requirements of the water-cooling jacket were derived by analyzing the results of the basic thermal properties. Next, the thermal characteristics analysis technique was established by using the equivalent model of the solenoid-typed pipe to be installed on the inside of the water-cooling jacket for 110kW-class IPMSM. Finally, a design model of 6kW-class water-cooling jacket was derived through the analysis of various design parameters.

• KCID journal
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• v.14 no.2
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• pp.236-244
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• 2007

Agricultural water demand in North Korea must be considered for the near-future investment in agricultural consolidation projects and to prepare for the future unification. Thus, the objective of this study is to estimate the agricultural water demand of paddy fieldss in North Korea. GIS data including land cover classification map, Thiessen network and administration maps of North Korea, and meteorological data were synthesized. In order to estimate paddy water demand for a 10-year return period, the FAO Blaney-Criddle method and the fixed effective rainfall ratio method were used. The results showed that 4.77 billion $\beta$(c)/year paddy water demand is required for the 512,400 ha of paddy fieldss. Paddy water demand in the three major regions - Hwanghaedo, Pyeongando, Hamgyeongnamdo - was estimated chargong 81.7 percent of total paddy water demand in North Korea.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.3
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• pp.279-287
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• 2005

In this study, I suggest an effective operation of waterwork facilities in large cities and a scientific method for utilizing water in water distribution systems. To achieve this goal, my simulation were carried out on data from Kwangju City using Pipenet '98, a pipe-network program. From this simulation, I examine the possibilities of application the system in large cities, comparing data measured at 33 hydraulic pressure monitoring places from waterwork enterprises. The result is coincident with that of waterwork enterprises, with about a 12.5% average error rate and $0.32kg/cm^2$ average deviation. The method and program I use here can be helpful in cities where there is a need to extend the waterwork facilities, or where there is a need to suspend the water supply, and/or there is an accident. The simulation shows how to expand waterwork facilities effectively, how to prevent accidents, and how to estimate the hydraulic pressure even in the areas without monitoring places.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.258-258
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• 2018

In this study, a hydraulic flow model and an error correction model are combined to improve the flood simulation accuracy. First, the hydraulic flow model is calibrated by optimizing the Manning's roughness coefficient that considers spatial and temporal variability. Then, an error correction model were used to correct the systematic errors of the calibrated hydraulic model. The error correction model is developed using Artificial Neural Networks (ANNs) that can estimate the systematic simulation errors of the hydraulic model by considering some state variables as inputs. The input variables are selected using parital mutual information (PMI) technique. It was found that the calibrated hydraulic model can simulate flood water levels with good accuracy. Then, the accuracy of estimated flood levels is improved further by using the error correction model. The method proposed in this study can be used to the flood control and water resources management as it can provide accurate water level eatimation.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1295-1303
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• 2022

As the role of water distribution networks (WDNs) becomes more important, identifying abnormal events (e.g., pipe burst) rapidly and accurately is required. Since existing approaches such as field equipment-based detection methods have several limitations, model-based methods (e.g., machine learning based detection model) that identify abnormal events using hydraulic simulation models have been developed. However, no previous work has examined the impact of data uncertainties on the results. Thus, this study compares the effects of measurement error-induced pressure data uncertainty in WDNs. An artificial neural network (ANN) is used to predict nodal pressures and measurement errors are generated by using cumulative density function inverse sampling method that follows Gaussian distribution. Total of nine conditions (3 input datasets × 3 output datasets) are considered in the ANN model to investigate the impact of measurement error size on the prediction results. The results have shown that higher data uncertainty decreased ANN model's prediction accuracy. Also, the measurement error of output data had more impact on the model performance than input data that for a same measurement error size on the input and output data, the prediction accuracy was 72.25% and 38.61%, respectively. Thus, to increase ANN models prediction performance, reducing the magnitude of measurement errors of the output pressure node is considered to be more important than input node.

• Korean Journal of Ecology and Environment
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• v.49 no.3
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• pp.208-214
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• 2016

We analyzed research trend and public perception related with tap water to identify major factors affecting low consumption of tap water. 805 research articles were collected for text mining analysis and 1,000 on-line questionnaires were surveyed to find social variables influencing tap water intake. Based on the word network analysis, research topics were divided into 4 major categories, 1) drinking water quality, 2) water fluoridation, 3) residual chlorine, and 4) micro-organism management. Compared with these major research topics, scientific studies of drinking behavior, or social perception were rather limited. 22.4% of total respondents used tap water as drinking water source, and only 1% drank tap water without further treatments (i.e. boiling, filtering). Experience of quality control report (B=0.392, p=0.046) and level of policy trust (B=1.002, p<0.0001) were influential factors on tap water drinking behavior. Age (B=0.020, p=0.002) and gender (B= - 1.843, p<0.0001) also showed significant difference. To increase the frequency of drinking the tap water by social members, the more scientific information of tap water quality and the water policy management should be clearly shared with social members.

• Journal of Korean Society of Disaster and Security
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• v.16 no.4
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• pp.75-84
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• 2023

Many developing countries face challenges in estimating long-term discharge due to the lack of hydrological data for water supply planning, making it difficult to establish a rational water supply plan for decision-making on water distribution. The study area, the Bandung region in Indonesia, is experiencing rapid urbanization and population concentration, leading to a severe shortage of freshwater. The absence of water reservoir prediction methods has resulted in a water supply rate of approximately 20%. In this study, we aimed to propose an approach for predicting water reservoirs in developing countries by analyzing water safety and potential water supply using the MODSIM (Modified SIMYLD) network model. To assess the suitability of the MODSIM model, we applied the unit hydrograph method to calculate long-term discharge based on 19 years of discharge data (2002-2020) from the Pataruman observation station. The analysis confirmed alignment with the existing monthly optimal operation curve. The analysis of power plant capacity revealed a difference of approximately 0.30% to 0.50%, and the water intake safety at the Pataruman point showed 1.64% for Q95% flow and 0.47% for Q355 flow higher. Operational efficiency, compared to the existing reservoir optimal operation curve, was measured at around 1%, confirming the potential of using the MODSIM network model for water supply evaluation and the need for water supply facilities.

• Journal of Environmental Policy
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• v.14 no.2
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• pp.27-47
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• 2015

In this paper, we present a new empirical method to estimate the environmental efficiency of decision making units. We propose a model with a new approach that describes a network process consisting of two stages, production and wastewater abatement based on the data extracted from 51 Korean industrial complexes. Taking into account the inter-dependency of two stages, we show a process how to decompose the environmental efficiency into production efficiency and abatement efficiency. Moreover, our new proposed method can be used to explain the information on network relationship between economic growth and environmental protection.