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상수도관망 재난관리 및 복구를 위한 데이터기반 이상탐지 방법론 개발
정동휘,안재현,Jung, Donghwi,Ahn, Jaehyun 한국수자원학회 2018 한국수자원학회논문집 Vol.51 No.8
상수도관의 파열은 과도한 압력, 노후화, 온도변화 나 지진 등에 의한 지반이동에 의해 발생한다. 상수도관 파열이 대규모 단수, 싱크홀 등과 같은 더 심각한 피해 이어지지 않도록 신속하게 탐지 및 대응하는 것이 중요하다. 본 연구에서는 상수도관 파열 탐지를 위해 개선 Western Electric Company (WECO) 방법을 개발하였다. 개선 WECO 방법은 통계적공정관리기법 중 하나인 기존 WECO 방법에 임계치 조정자(w)를 추가하여 대상 네트워크에 적합한 이상탐지 의사결정을 할 수 있도록 했다. 개발된 개선 WECO 방법을 미국 텍사스 오스틴 관망에 적용 및 검증하였다. 상수도관 파열 발생 시 측정한 비정상데이터와 수요량 변동만 고려한 정상데이터를 이용하여 기존 및 개선 WECO 방법을 비교하였다. 최적 임계치 조정자 w값을 결정하기 위해 민감도 분석을 수행하였으며, 다양한 계측시간 간격 데이터(dt = 5, 10, 15분 등)의 영향도 분석하였다. 각 경우 별 탐지성능은 탐지확률, 오경보확률, 평균탐지시간을 계산하여 비교하였다. 본 연구에서는 도출된 결과를 바탕으로 WECO 방법을 실제 상수도관 파열 탐지에 적용하기 위한 가이드라인을 제공한다. Water distribution system (WDS) pipe bursts are caused from excessive pressure, pipe aging, and ground shift from temperature change and earthquake. Prompt detection of and response to the failure event help prevent large-scale service interruption and catastrophic sinkhole generation. To that end, this study proposes a improved Western Electric Company (WECO) method to improve the detection effectiveness and efficiency of the original WECO method. The original WECO method is an univariate Statistical Process Control (SPC) technique used for identifying any non-random patterns in system output data. The improved WECO method multiples a threshold modifier (w) to each threshold of WECO sub-rules in order to control the sensitivity of anomaly detection in a water distribution network of interest. The Austin network was used to demonstrated the proposed method in which normal random and abnormal pipe flow data were generated. The best w value was identified from a sensitivity analysis, and the impact of measurement frequency (dt = 5, 10, 15 min etc.) was also investigated. The proposed method was compared to the original WECO method with respect to detection probability, false alarm rate, and averaged detection time. Finally, this study provides a set of guidelines on the use of the WECO method for real-life WDS pipe burst detection.
박근영,정동휘,전상훈,Park, Geunyeong,Jung, Donghwi,Jun, Sanghoon 한국수자원학회 2021 한국수자원학회논문집 Vol.54 No.10
This work introduces a new approach that classifies individual household water usage by examining the characteristics of smart meter end-user demand data. Here, one of the most well-known unsupervised machine learning, K-means algorithm, is applied to classify water consumptions by each household. The intensity and duration of end-user demands are used as main features to determine the households with similar water consumption pattern. The results showed that 21 households are classified into 13 clusters with each cluster having one, two, three, or five houses. The reasoning why multiple households are classified into the same cluster is described in this paper with respect to the collected data and end-user water consumption behavior.
상수도 시스템 지진 신뢰성의 합리적 평가를 위한 적정 지반운동예측식 결정
최정욱,강두선,정동휘,이찬욱,유도근,조성배,Choi, Jeongwook,Kang, Doosun,Jung, Donghwi,Lee, Chanwook,Yoo, Do Guen,Jo, Seong-Bae 한국수자원학회 2020 한국수자원학회논문집 Vol.53 No.9
The water supply system has a wider installation range and various components of it than other infrastructure, making it difficult to secure stability against earthquakes. Therefore, it is necessary to develop methods for evaluating the seismic performance of water supply systems. Ground Motion Prediction Equation (GMPE) is used to evaluate the seismic performance (e.g, failure probability) for water supply facilities such as pump, water tank, and pipes. GMPE is calculated considering the independent variables such as the magnitude of the earthquake and the ground motion such as PGV (Peak Ground Velocity) and PGA (Peak Ground Acceleration). Since the large magnitude earthquake data has not accumulated much to date in Korea, this study tried to select a suitable GMPE for the domestic earthquake simulation by using the earthquake data measured in Korea. To this end, GMPE formula is calculated based on the existing domestic earthquake and presented the results. In the future, it is expected that the evaluation will be more appropriate if the determined GMPE is used when evaluating the seismic performance of domestic waterworks. Appropriate GMPE can be directly used to evaluate hydraulic seismic performance of water supply networks. In other words, it is possible to quantify the damage rate of a pipeline during an earthquake through linkage with the pipe failure probability model, and it is possible to derive more reasonable results when estimating the water outage or low-pressure area due to pipe damages. Finally, the quantifying result of the seismic performance can be used as a design criteria for preparing an optimal restoration plan and proactive seismic design of pipe networks to minimize the damage in the event of an earthquake.
적대적 학습 기반 오토인코더(ATAE)를 이용한 다차원 상수도관망 데이터 생성
김세형,전상훈,정동휘,Kim, Sehyeong,Jun, Sanghoon,Jung, Donghwi 한국수자원학회 2023 한국수자원학회논문집 Vol.56 No.7
Recent advancements in data measuring technology have facilitated the installation of various sensors, such as pressure meters and flow meters, to effectively assess the real-time conditions of water distribution systems (WDSs). However, as cities expand extensively, the factors that impact the reliability of measurements have become increasingly diverse. In particular, demand data, one of the most significant hydraulic variable in WDS, is challenging to be measured directly and is prone to missing values, making the development of accurate data generation models more important. Therefore, this paper proposes an adversarially trained autoencoder (ATAE) model based on generative deep learning techniques to accurately estimate demand data in WDSs. The proposed model utilizes two neural networks: a generative network and a discriminative network. The generative network generates demand data using the information provided from the measured pressure data, while the discriminative network evaluates the generated demand outputs and provides feedback to the generator to learn the distinctive features of the data. To validate its performance, the ATAE model is applied to a real distribution system in Austin, Texas, USA. The study analyzes the impact of data uncertainty by calculating the accuracy of ATAE's prediction results for varying levels of uncertainty in the demand and the pressure time series data. Additionally, the model's performance is evaluated by comparing the results for different data collection periods (low, average, and high demand hours) to assess its ability to generate demand data based on water consumption levels.