• Environmental Engineering Research
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• v.14 no.2
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• pp.102-110
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• 2009

Application of artificial intelligence (AI) approaches in eco-environmental modeling has gradually increased for the last decade. Comprehensive understanding and evaluation on the applicability of this approach to eco-environmental modeling are needed. In this study, we reviewed the previous studies that used AI-techniques in eco-environmental modeling. Decision Tree (DT) and Artificial Neural Network (ANN) were found to be major AI algorithms preferred by researchers in ecological and environmental modeling areas. When the effect of the size of training data on model prediction accuracy was explored using the data from the previous studies, the prediction accuracy and the size of training data showed nonlinear correlation, which was best-described by hyperbolic saturation function among the tested nonlinear functions including power and logarithmic functions. The hyperbolic saturation equations were proposed to be used as a guideline for optimizing the size of training data set, which is critically important in designing the field experiments required for training AI-based eco-environmental modeling.

• Journal of Environmental Impact Assessment
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• v.22 no.5
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• pp.537-546
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• 2013

In the Seoul Metropolitan Area(SMA) photochemical air pollutants, nitrogenic compound and particulate matters have increased substantially due to mobile sources, power plants and so on. Therefore 'Special Act on Seoul Metropolitan Air Quality Improvement' was enacted on 2003 in order to improve air quality in the SMA. According to the Special Act, Central and local government have developed the state implementation plan(SIP) to reduce air pollutant emissions from various local sources. One of the key elements of the SIP development is the air quality modeling since modeling results can be used to establish emissions control strategies as well as to demonstrate attainment of air quality goals for ozone, particulate matter, and so on. Air quality modeling, therefore, can be usefully utilized to investigate the effects of government's efforts according to control strategies or measures. Using the air quality model, we can determine whether the implementation plan should be revised or not. A number of questions, however, has been raised concerning accuracy, consistency and transparency of modeling results because if we do not trust modeling results, all the measures dependent on modeling becomes in vain. So, without dealing with these questions, we can not guarantee the reliability and utilizability of air quality modeling results. In this study, we tried to establish standard methodology for air quality modeling in order to ensure consistency and transparency of modeling results used in the development and evaluation of national air policy. For this purpose, we established air quality modeling guideline to provide or recommend modeling procedures, vertical and horizontal domains, input data of meteorological and air quality modeling and so on.

• Environmental Engineering Research
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• v.16 no.3
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• pp.113-119
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• 2011

This study traces the origin, evolution, and current state-of-the-art of engineering-oriented water-quality management and modeling. Three attributes of polluted water underlie human concerns for water quality: rubbish (aesthetic impairment), stink (ecosystem impairment), and death (public health impairment). The historical roots of both modern environmental engineering and water-quality modeling are traced to the late nineteenth and early twentieth centuries when European and American engineers worked to control and manage urban wastewater. The subsequent evolution of water-quality modeling can be divided into four stages related to dissolved oxygen (1925-1960), computerization (1960-1970), eutrophication (1970-1977) and toxic substances (1977-1990). Current efforts to integrate these stages into unified holistic frameworks are described. The role of water-quality management and modeling for developing economies is outlined.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.621-635
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• 2020

Water quality models are scientific tools that simulate and interpret the relationship between physical, chemical and biological reactions to external pollutant loads in water systems. They are actively used as a key technology in environmental water management. With recent advances in computational power, water quality modeling technology has evolved into a coupled three-dimensional modeling of hydrodynamics, water quality, and ecological inputs. However, there is uncertainty in the simulated results due to the increasing model complexity, knowledge gaps in simulating complex aquatic ecosystem, and the distrust of stakeholders due to nontransparent modeling processes. These issues have become difficult obstacles for the practical use of water quality models in the water management decision process. The objectives of this paper were to review the theoretical background, needs, and development status of water quality modeling technology. Additionally, we present the potential future directions of water quality modeling technology as a scientific tool for national environmental water management. The main development directions can be summarized as follows: quantification of parameter sensitivities and model uncertainty, acquisition and use of high frequency and high resolution data based on IoT sensor technology, conjunctive use of mechanistic models and data-driven models, and securing transparency in the water quality modeling process. These advances in the field of water quality modeling warrant joint research with modeling experts, statisticians, and ecologists, combined with active communication between policy makers and stakeholders.

• Environmental Engineering Research
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• v.14 no.4
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• pp.200-210
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• 2009

The modern era of water quality modeling in the United States began in the 1960s. Pushed by advances in computer technology as well as environmental sciences, water quality modeling evolved through five broad periods: (1) initial model development with mainframe computers (1960s - mid 1970s), (2) model refinement and generalization with minicomputers (mid 1970s - mid 1980s), (3) model standardization and support with microcomputers (mid 1980s - mid 1990s), (4) better model access and performance with faster desktop computers running Windows and local area networks linked to the Internet (mid 1990s - early 2000s), and (5) model integration and widespread use of the Internet (early 2000s - present). Improved computer technology continues to drive improvements in water quality models, including more detailed environmental analysis (spatially and temporally), better user interfaces and GIS software, more accessibility to environmental data from on-line repositories, and more robust modeling frameworks linking hydrodynamics, water quality, watershed and atmospheric models. Driven by regulatory needs and advancing technology, water quality modeling will continue to improve to better address more complicated water bodies and pollutant types, and more complicated management questions. This manuscript describes historical trends in water quality model development in the United States, reviews current efforts, and projects promising future directions.

• Journal of Environmental Impact Assessment
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• v.12 no.4
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• pp.245-257
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• 2003

$SO_2$ concentrations in the Seoul Metropolitan Area (SMA) were predicted by the regional segment ISCST3 modeling. The SMA was segmented by three modeling regions where the weather monitoring station exists since the area of the SMA, approximately $100km{\times}100km$, is too wide to be modeled by one modeling domain. The predicted concentrations by the model were compared with the measured concentrations at 39 air monitoring stations located in the SMA to validate the ISCST3 modeling coupled with the regional segment approach. The predicted concentrations by the regional segment method showed better performance in depicting the measurements than those by the non-segment ISCST3 modeling. The correction methods of the calculated concentrations reviewed were here the correlation method by the first order linear equation and the ratio method of observed to calculated concentrations. The corrected concentrations by two methods showed good agreement with the measured data. The ratio method was, however, easily applicable to the concentration correction in case of a wide modeling region considered in this study.

• International conference on construction engineering and project management
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• 2017.10a
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• pp.242-244
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• 2017

Excavation for construction is implemented in natural geographical terrain using a variety of construction equipment. Therefore, 3D excavation simulation requires integration of geographical and equipment modeling. This paper proposes a technique that integrates geographical and equipment modeling for 3D simulations of construction excavation. The geographical model uses a digital map to show ground surface changes during excavation and the equipment model shows equipment movement and placement. This combination produced a state of the art 3D simulation environment that can be used for machine guidance. An equipment operator can use the 3D excavation simulation to help construction equipment operators with decisions during excavation work and consequently improve productivity.

• Asian Journal of Atmospheric Environment
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• v.11 no.4
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• pp.330-343
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• 2017

Climate change is an important issue, with many researches examining not only future climatic conditions, but also the interaction of climate and air quality. In this study, a new version of the emissions processing software tool - Python-based PRocessing Operator for Climate and Emission Scenarios (PROCES) - was developed to support climate and atmospheric chemistry modeling studies. PROCES was designed to cover global and regional scale modeling domains, which correspond to GEOS-Chem and CMAQ/CAMx models, respectively. This tool comprises of one main system and two units of external software. One of the external software units for this processing system was developed using the GIS commercial program, which was used to create spatial allocation profiles as an auxiliary database. The SMOKE-Asia emissions modeling system was linked to the main system as an external software, to create model-ready emissions for regional scale air quality modeling. The main system was coded in Python version 2.7, which includes several functions allowing general emissions processing steps, such as emissions interpolation, spatial allocation and chemical speciation, to create model-ready emissions and auxiliary inputs of SMOKE-Asia, as well as user-friendly functions related to emissions analysis, such as verification and visualization. Due to its flexible software architecture, PROCES can be applied to any pregridded emission data, as well as regional inventories. The application results of our new tool for global and regional (East Asia) scale modeling domain under RCP scenario for the years 1995-2006, 2015-2025, and 2040-2055 was quantitatively in good agreement with the reference data of RCPs.

• Journal of Environmental Science International
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• v.24 no.12
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• pp.1691-1703
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• 2015

When constructing tunnels, it is important to understand structural, geological and hydrogeological conditions. Geumgeong tunnel that has been constructed in Mt. Geumjeong for the Gyeongbu express railway induced rapid drawdown of groundwater in the tunnel construction area and surroundings. This study aimed to analyze groundwater flow system and baseflow using long-term monitoring and groundwater flow modeling around Geumgeong tunnel. Field hydraulic tests were carried out in order to estimate hydraulic conductivity, transmissivity, and storativity in the study area. Following the formula of Turc and groundwater flow modeling, the annual evapotranspiration and recharge rate including baseflow were estimated as 48% and 23% compared to annual precipitation, respectively. According to the transient modeling for 12 years after tunnel excavation, baseflow was estimated as $9,796-9,402m^3/day$ with a decreasing tendency.

• Korean Chemical Engineering Research
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• v.44 no.6
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• pp.579-587
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• 2006

Recently, each country has been trying to promote Eco-Industrial Park (EIP) development for industrial sustainability. Technological modeling is required to realize EIP practically even though the project contains the political concerns for many companies, government, and self-governing bodies. The four main technologies of the EIP developments include energy exchange, material flow analysis, water pinch, and life cycle assessment. Material flow analysis (MFA) methodology can be utilized in EIP modeling in view of the fact that the analysis of material flows and the optimized modeling are major purposes for the technological modeling of EIP. Through MFA methodology in POHANG EIP project, how to apply MFA modeling to EIP modeling and how to utilize software for MFA modeling are shown in this research.