Conventional Monte Carlo simulation-based methods for seismic risk assessment of water networks often require excessive computational time costs due to the hydraulic analysis. In this study, an Artificial Neural Network-based surrogate model was proposed to efficiently evaluate the flow-based system reliability of water distribution networks. The surrogate model was constructed with appropriate training parameters through trial-and-error procedures. Furthermore, a deep neural network with hidden layers and neurons was composed for the high-dimensional network. For network training, the input of the neural network was defined as the damage states of the k-dimensional network facilities, and the output was defined as the network system performance. To generate training data, random sampling was performed between earthquake magnitudes of 5.0 and 7.5, and hydraulic analyses were conducted to evaluate network performance. For a hydraulic simulation, EPANET-based MATLAB code was developed, and a pressure-driven analysis approach was adopted to represent an unsteady-state network. To demonstrate the constructed surrogate model, the actual water distribution network of A-city, South Korea, was adopted, and the network map was reconstructed from the geographic information system data. The surrogate model was able to predict network performance within a 3% relative error at trained epicenters in drastically reduced time. In addition, the accuracy of the surrogate model was estimated to within 3% relative error (5% for network performance lower than 0.2) at different epicenters to verify the robustness of the epicenter location. Therefore, it is concluded that ANN-based surrogate model can be utilized as an alternative model for efficient seismic risk assessment to within 5% of relative error.
Traffic represents one of the largest sources of primary air pollutants in urban area. As a consequence, numerous abatement strategies are being pursued to decrease the ambient concentration of pollutants. A characteristics of most of the these strategies is a requirement for accurate data on both the quantity and spatial distribution of emissions to air in the form of an atmospheric emission inventory database. In the case of traffic pollution, such an inventory must be compiled using activity statistics and emission factors for vehicle types. The majority of inventories are compiled using passive data from either surveys or transportation models and by their very nature tend to be out-of-date by the time they are compiled. The study of current trends is towards integrating urban traffic control systems and assessments of the environmental effects of motor vehicles. In this study, a model of vehicle emission calculation by using real-time traffic data was studied. Traffic data, which are required on a street-by-street basis, is obtained from induction loops of traffic control system. It is possible that characteristics of hourly air pollutants emission rates is obtained from hourly traffic volume and speed. An emission rates model is allocated with a high resolution space by using geographic information system (GIS). Vehicle emission model was developed with a high resolution spatial, gridded and hourly emission rates.
Journal of the Korean Association of Geographic Information Studies
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v.21
no.3
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pp.176-188
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2018
Modern urban problems are increasingly becoming a market mix that can not be solved by the power of a single department and the necessity of establishing a cooperation system based on data communication between departments is increasing. Therefore, this study analyzed Busan metropolitan city's IT projects from 2014 to 2018 in order to understand the utilization and sharing status of departmental data from the viewpoint that cooperation between departments can start from the sharing of data with high common utilization. In addition, based on the results of the FGI(Focus Group Interview) conducted for the officials of the department responsible for the informatization project, we verified the results of data status analysis. At the same time, we figured out the necessity of data link between departments through SNA(Social Network Analysis) and presented data that should be shared first in the future. As a result, most of the information systems currently use limited data only within the department that produced the data. Most of the linked data was concentrated in the information department. Therefore, this study suggested the following solutions. First, in order to prevent overlapping investments caused by the operation of individual departments and share information, it is necessary to build a small platform to tie the departments, which have high connectivity with each other, into small blocks. Second, a local level process is needed to develop data standards as an extension of national standards in order to expand the information to be used in various fields. Third, as another solution, we proposed a system that can integrate various types of information based on address and location information through application of cloud-based GIS platform. The results of this study are expected to contribute to build a cooperation system between departments through expansion of information sharing with cost reduction.
In accordance with the government's stance of actively promoting intelligent administrative service policies through data utilization, in the disaster and safety management field, it also is proceeding with disaster and safety management policies utilizing data and constructing systems for responding efficiently to new and complex disasters and establishing scientific and systematic safety policies. However, it is difficult to quickly and accurately grasp the on-site situation in the event of a disaster, and there are still limitations in providing information necessary for situation judgment and response only by displaying vast data. This paper focuses on deriving specific needs to make disaster situation management work more intelligent and efficient by utilizing intelligent information technology. Through individual interviews with workers at the Central Disaster and Safety Status Control Center, we investigated the scope of disaster situation management work and the main functions and usability of the geographic information system (GIS)-based integrated situation management system by practitioners in this process. In addition, the data built in the system was reclassified according to purpose and characteristics to check the status of data in the GIS-based integrated situation management system. To derive needed to make disaster situation management more intelligent and efficient by utilizing intelligent information technology, 3 strategies were established to quickly and accurately identify on-site situations, make data-based situation judgments, and support efficient situation management tasks, and implementation tasks were defined and task priorities were determined based on the importance of implementation tasks through analytic hierarchy process (AHP) analysis. As a result, 24 implementation tasks were derived, and to make situation management efficient, it is analyzed that the use of intelligent information technology is necessary for collecting, analyzing, and managing video and sensor data and tasks that can take a lot of time of be prone to errors when performed by humans, that is, collecting situation-related data and reporting tasks. We have a conclusion that among situation management intelligence strategies, we can perform to develop technologies for strategies being high important score, that is, quickly and accurately identifying on-site situations and efficient situation management work support.
We analyzed the monthly and annual trends of the meteorological factors(wind speeds and directions and air temperatures) measured at an automated synoptic observation system (ASOS) and fine particle (PM10 and PM2.5) concentrations measured at the air quality monitoring systems(AQMSs) in Suwon. In addition, we investigated how the fine particle concentrations were related to the meteorological factors as well as urban morphological parameters (fractions of building volume and road area). We calculated the total volume of buildings and the total area of the roads in the area of 2 km × 2 km centered at each AQMS using the geographic information system and environmental geographic information system. The analysis of the meteorological factors showed that the dominant wind directions at the ASOS were westerly and northwesterly and that the average wind speed was strong in Spring. The measured fine particle concentrations were low in Summer and early Autumn (July to September) and high in Spring and Winter. In 2020, the annual mean fine particle concentration was lowest at most AQMSs. The fine particle concentrations were negatively and weakly correlated with the measured wind speeds and air temperatures (the correlation between PM2.5 concentrations and air temperatures was relatively strong). In Suwon city, at least for 6 AQMSs except for the RAQMS 131116 and AQMS 131118, the PM10 concentrations were affected mainly by the transport from outside rather than primary emission from mobile sources or wind speed decrease caused by buildings and, in the case of PM2.5, vise versa.
Kim, Seoung-Hyeon;Moon, Byung-Hyun;Song, Bong-Geun;Park, Kyung-Hun
Journal of the Korean Association of Geographic Information Studies
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v.22
no.3
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pp.10-20
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2019
Irregular rainfall caused by climate change, in combination with non-point pollution, can cause water systems worldwide to suffer from frequent eutrophication and algal blooms. This type of water pollution is more common in agricultural prone to water system inflow of non-point pollution. Therefore, in this study, the correlation between Unmanned Aerial Vehicle(UAV) multi-spectral images and total phosphorus, total nitrogen, and chlorophyll-a with indirect association of algal blooms, was analyzed to identify the usability of UAV image to identify water quality characteristics in agricultural streams. The analysis the vegetation index Normalized Differences Index (NDVI), the Normalized Differences Red Edge(NDRE), and the Chlorophyll Index Red Edge(CIRE) for the detection of multi-spectral images and algal blooms collected from the target regions Yang cheon and Hamyang Wicheon. The analysis of the correlation between image values and water quality analysis values for the water sampling points, total phosphorus at a significance level of 0.05 was correlated with the CIRE(0.66), and chlorophyll-a showed correlation with Blue(-0.67), Green(-0.66), NDVI(0.75), NDRE (0.67), CIRE(0.74). Total nitrogen was correlated with the Red(-0.64), Red edge (-0.64) and Near-Infrared Ray(NIR)(-0.72) wavelength at the significance level of 0.05. The results of this study confirmed a significant correlations between multi-spectral images collected through UAV and the factors responsible for water pollution, In the case of the vegetation index used for the detection of algal bloom, the possibility of identification of not only chlorophyll-a but also total phosphorus was confirmed. This data will be used as a meaningful data for counterplan such as selecting non-point pollution apprehensive area in agricultural area.
Attempts were made to analyze the national policy of soil contamination prevention and management of inactive or abandoned metal (IAM) mines in Korea. This approach focused on legal systems and legislation, remediation technology development, and the arrangement or distribution of budgets pertaining to national policy since the mid 1990's. Prevention of Mining Damage and Recovery Act enacted. Defines the roles, responsibility and budget of the government when recovering mine damages. However, in 2005 there still remains to improve the national policy of soil contamination prevention and management of IAM mines. Analysis of national and industrialized foreign countries including the United States, the United Kingdom, and the Netherlands suggest the following improvements: i) arranging distinct regulations between strict and non-strict liability criteria for potentially responsible parties; limiting innocent and non-strict liability depending on the period of incurred mining activity, ii) enhancing participation of local communities by enforcing law and legislation, iii) establishing a national database system of (potentially) IAM contaminated sites based on the Website-Geographic Information System, iv) carrying out site-specific risk assessments and remediation of IAM contaminated sites, v) preparation and distribution of clean-up fund at mine sites adequately, and vi) technology development for the cleaning of IAM contaminated sites; awarding positive incentives of a legal nature for participants applying newly developed technology in IAM mines.
Graphical processing unit (GPU) contains many arithmetic logic units (ALUs). Because many ALUs can be exploited to process parallel processing, GPU provides efficient data processing. The spatial data require many geographic coordinates to represent the shape of them in a map. The coordinates are usually stored as geodetic longitude and latitude. To display a map in 2-dimensional Cartesian coordinate system, the geodetic longitude and latitude should be converted to the Universal Transverse Mercator (UTM) coordinate system. The conversion to the other coordinate system and the rendering process to represent the converted coordinates to screen use complex floating-point computations. In this paper, we propose a parallel processing technique that processes the conversion and the rendering using the GPU to improve the performance. Large spatial data is stored in the disk on files. To process the large amount of spatial data efficiently, we propose a technique that merges the spatial data files to a large file and access the file with the method of memory mapped file. We implement the proposed technique and perform the experiment with the 747,302,971 points of the TIGER/Line spatial data. The result of the experiment is that the conversion time for the coordinate systems with the GPU is 30.16 times faster than the CPU only method and the rendering time is 80.40 times faster than the CPU.
Kim, Seong-Gon;Kim, Seung Hee;Lim, Chul-Hee;Na, Seong-Kyun;Park, Sang Seo;Kim, Jaemin;Lee, Yun Gon
Atmosphere
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v.31
no.2
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pp.241-249
/
2021
A smart city utilizes data collected from various sensors through the internet of things (IoT) and improves city operations across the urban area. Recently substantial research is underway to examine all aspects of data that requires for the smart city operation. Atmospheric data are an essential component for successful smart city implementation, including Urban Air Mobility (UAM), infrastructure planning, safety and convenience, and traffic management. Unfortunately, the current level of conventional atmospheric data does not meet the needs of the new city concept. New and innovative approaches to developing high spatiotemporal resolution of observational and modeling data, resolving the complex urban structure, are expected to support the future needs. The geographic information system (GIS) integrates the atmospheric data with the urban structure and offers information system enhancement. In this study we proposed the necessity and applicability of the high resolution urban meteorological dataset based on heavy fog cases in the smart city region (e.g., Sejong and Pusan) in Korea.
An area's topography refers to the shape of the earth's surface, described by its elevation, slope, and aspect, among other features. The topographical conditions determine energy flowsthat move water and energy from higher to lower elevations, such as how much solar energy will be received and how much wind or rain will affect it. Another common factor, the topographic wetness index (TWI), is a calculation in digital elevation models of the tendency to accumulate water per slope and unit area, and is one of the most widely referenced hydrologic topographic factors, which helps explain the location of forest vegetation. Analyses of topographical factors can be calculated using a geographic information system (GIS) program based on digital elevation model (DEM) data. Recently, a large number of free open source software (FOSS) GIS programs are available and developed for researchers, industries, and governments. FOSS GIS programs provide opportunitiesfor flexible algorithms customized forspecific user needs. The majority of biodiversity in island areas exists at about 20% higher elevations than in land ecosystems, playing an important role in ecological processes and therefore of high ecological value. However, island areas are vulnerable to disturbances and damage, such as through climate change, environmental pollution, development, and human intervention, and lacks systematic investigation due to geographical limitations (e.g. remoteness; difficulty to access). More than 4,000 of Korea's islands are within a few hours of its coast, and 88% are uninhabited, with 52% of them forested. The forest ecosystems of islands have fewer encounters with human interaction than on land, and therefore most of the topographical conditions are formed naturally and affected more directly by weather conditions or the environment. Therefore, the analysis of forest topography in island areas can be done more precisely than on its land counterparts, and therefore has become a major focus of attention in Korea. This study is focused on calculating the performance of different topographical factors using FOSS GIS programs. The test area is the island forests in Korea's south and the DEM of the target area was processed with GRASS GIS and SAGA GIS. The final slopes and TWI maps were produced as comparisons of the differences between topographic factor calculations of each respective FOSS GIS program. Finally, the merits of each FOSS GIS program used to calculate the topographic factors is discussed.
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