• 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.

• Journal of the Korean GEO-environmental Society
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• v.24 no.4
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• pp.23-29
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• 2023

Ground subsidence shows a mechanism in which the upper ground collapses due to the formation of a cavity due to the movement of soil particles in the ground due to the formation of a waterway because of damage to the water supply/sewer pipes. As a result, cavity is created in the ground and the upper ground is collapsing. Therefore, ground subsidence frequently occurs mainly in downtown areas where a large amount of underground facilities are buried. Accordingly, research to predict the risk of ground subsidence is continuously being conducted. This study tried to present a ground subsidence risk prediction model for two districts of ○○ city. After constructing a data set and performing preprocessing, using the property data of underground facilities in the target area (year of service, pipe diameter), density of underground facilities, and ground subsidence history data. By applying the dataset to the machine learning model, it is evaluated the reliability of the selected model and the importance of the influencing factors used in predicting the ground subsidence risk derived from the model is presented.

• Journal of the Korean Geotechnical Society
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• v.37 no.12
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• pp.117-125
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• 2021

The occurrence of ground subsidence and sinkhole in downtown areas, which threatens the safety of citizens, has been frequently reported. Among the various mechanisms of a sinkhole, soil erosion through the damaged part of the sewer pipe was found to be the main cause in Seoul. In this study, a random forest model for predicting the occurrence of sinkholes caused by damaged sewer pipes based on sewage pipe information was trained using the information on the sewage pipe and the locations of the sinkhole occurrence case in Seoul. The random forest model showed excellent performance in the prediction of sinkhole occurrence after the optimization of its hyperparameters. In addition, it was confirmed that the sewage pipe length, elevation above sea level, slope, depth of landfill, and the risk of ground subsidence were affected in the order of sewage pipe information used as input variables. The results of this study are expected to be used as basic data for the preparation of a sinkhole susceptibility map and the establishment of an underground cavity exploration plan and a sewage pipe maintenance plan.

• Journal of the Korean GEO-environmental Society
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• v.23 no.4
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• pp.5-10
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• 2022

Recently, ground subsidence has been continuously occurring in downtown areas, threatening the safety of citizens. Various underground facilities such as water and sewage pipelines and communication pipelines are buried under the road. It is reported that the cause of ground subsidence is the deterioration of various facilities and the reckless development of the underground. In particular, it is known that the biggest cause of ground subsidence is the aging of sewage pipelines. As an existing study related to this, several representative factors of sewage pipelines were selected and a study to predict the risk of ground subsidence through statistical analysis has been conducted. In this study, a data SET was constructed using the characteristics of OO city's sewage pipe characteristics and ground subsidence data, The data set constructed from the characteristics of the sewage pipe of OO city and the location of the ground subsidence was used. The goal of this study was to present a classification model for the occurrence of ground subsidence according to the characteristics of sewage pipes through machine learning. In addition, the importance of each sewage pipe characteristic affecting the ground subsidence was calculated.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.463-463
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• 2023

국내 수도관 보급률이 증가하면서 기존 노후화된 수도관들과 추가로 노후화된 수도관들이 증가하고 있다. 경과년수가 오래된 시설이 증가하는 것은 잠재적인 사고발생 위험을 증가시킨다. 실제 노후화된 상수도 시설물로 인해 단수, 누수, 수질오염, 지반함몰 발생이 증가하는 추세이다. 이러한 현상들은 시민들의 생활과 안전, 경제활동에 직접적인 영향을 끼치기 때문에 이에 대한 대책 마련이 시급한 상태이다. 본 연구에서는 AI를 기반으로 상수도관의 노후도 및 위험도를 예측하는 모델을 설계하고자 하였다. 대상지역을 인천광역시 서구로 선정하여 유량과 수질의 실시간 계측데이터를 수집하였다. 딥러닝 기법 중 하나인 LSTM(Long Short-Term Memory)을 이용하여 데이터를 예측하였고, 결정계수(R²)와 RMSE(Root Mean Square Error)로 학습데이터와 검증데이터의 비율을 정하여 예측도를 평가하였다. 유량과 수질 데이터 중 80%는 학습데이터로 20%는 검증 데이터로 분리하였고, LSTM의 셀과레이어 수를 해석에 적합한 범위로 설정한 결과, 실제값과 예측값이 높은 상관성을 보이는 것으로 나타났다. 예측된 유량 및 수질의 결과는 상수도 관리에 중요한 정보를 제공하며, 사고 위험도 평가와 관 노후화에 따른 대응력을 향상시키는 데 도움이 될 것으로 판단된다.