• Title/Summary/Keyword: Sewer Facility Map

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A Study on the Sewerage Facility Management Technique based on GIS (GIS를 이용한 하수도 시설물 관리 방법에 관한 연구)

  • 최재화;박희주;이홍술
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.11 no.2
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    • pp.43-51
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    • 1993
  • It is very important entity that a map of urban facility is in civic life and development of city. But now, there are many needs of computerization which was used for management of urban facility map because inefficiency was produced due to management of urban facility map by hand. This paper, it was showed that a processing of establishing database in computer by applying GIS. The used sample area in studying in Ansan city in Kyunggi province and a sewer pipe which was in there adapted to establish database in computer. A map of administration district, which is 1:38,000, a map of complex urban planning of city, which is 1:3,000, and the facility map were used for the input of spatial data and the address, the year when sewer pipe was burried, and the quality of pipe, the pipe diameter, and the length of pipe were used as attribute data. Relational structure was used for establishment of database then, searching, analyzing, and processing were possible. Additionary, the result of this studying can be used for replacement of a old sewer pipe, a material quantity which is related with repairement of sewer pipe, and calculation of time, cost by application program framed by Fortran.

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A Study on the Automatic Inspection of Sewer Facility Map (하수도시설물도 자동 검수 방안 연구)

  • Kim, Chang-Hwan;Ohk, Won-Soo;Yoo, Jae-Yong
    • Journal of the Korean Association of Geographic Information Studies
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    • v.9 no.2
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    • pp.67-78
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    • 2006
  • Local governments began to construct geographic information system to improve government productivity and performance. In support, central government organized a national commission for GIS. The master plan by NGIS has been the base for local government to participate in the construction of GIS at the local level in the under ground facilities management including water and sewers. The challenge faced by sewer facility managers includes controlling 'data accuracy'. The input for sewer data handling for efficient performance in local government requires accurate data. However data manipulation to get the 'good quality' data can be burdensome. Thus, the aim of this research is to provide the appropriate tool to guarantee the high quality of digital data in sewer facility management. It is helpful to pass the data examination by government as well as to insure confidence of decision and data analysis works in local government. In this research, error types of sewer data were classified and pointed the limitation of traditional examination methods. Thus this research suggested more improved method for finding and correcting errors in data input using sewer volume analysis and prediction model as immigrating sewer facility management work to Geographic Information System.

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Development of Machine Learning Model to Predict the Ground Subsidence Risk Grade According to the Characteristics of Underground Facility (지하매설물 속성을 활용한 기계학습 기반 지반함몰 위험도 예측모델 개발)

  • Lee, Sungyeol;Kang, Jaemo;Kim, Jinyoung
    • Journal of the Korean GEO-environmental Society
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    • v.23 no.8
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    • pp.5-10
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    • 2022
  • Ground Subsidence has been continuously occurring in densely populated downtown. The main cause of ground subsidence is the damaged underground facility like sewer. Currently, ground subsidence is being dealt with by discovering cavities in ground using GPR. However, this consumes large amount of manpower and cost, so it is necessary to predict hazardous area for efficient operation of GPR. In this study, ◯◯city is divided into 500 m×500 m grids. Then, data set was constructed using the characteristics of the underground facility and ground subsidence in grids. Data set used to machine learning model for ground subsidence risk grade prediction. The purposed model would be used to present a ground subsidence risk map of target area.