상수관로 누수 감지를 위한 머신러닝 모델의 적용
Application of Machine Learning Models for Water Pipeline Leakage Detection
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초록
본 연구에서는 상수도관 누수 감지를 위한 머신러닝 모델의 적용 가능성을 평가하였다. 상수관로 누수탐지 공개 데이터셋을 활용해 개발한 극한 경사 부스팅(XGBoost), 경사 부스팅 머신(GBM), LightGBM, 범주형 부스팅(CatBoost), 적응형 부스팅(AdaBoost), 랜덤 포레스트(RF) 머신러닝 모델을 혼동 행렬과성능 지수를 활용한 분류 성능을 기준으로 평가 및 비교를 하였다. 그 결과 최신 부스팅 모델인XGBoost, GBM, LightGBM, CatBoost는 RF 및 AdaBoost 모델에 비해 우수한 분류 성능을 나타내었다. 최신 부스팅 모델의 성능은 비슷하지만, LightGBM 모델(정확도 = 0.960, 정밀도 = 0.941, 재현율 = 0.955, F1 점수 = 0.948, 특이도 = 0.970)이 가장 우수한 성능을 나타내는 것으로 분석되었다. 따라서 최신부스팅 머신러닝 모델은 빅데이터 기반 상수도관 누수 감지 시스템을 개발하고 상수도관 누수 위험을관리하는 효과적인 예측 도구로 사용할 수 있을 것으로 판단된다.
Abstract
The applicability of machine learning models for detecting water pipeline leakage was evaluated in this study. The machine learning models, extreme gradient boosting (XGBoost), gradient boosting machine (GBM), LightGBM, categorical boosting (CatBoost), adaptive boosting (AdaBoost), and random forest (RF) models, which were developed using the open dataset of water pipeline leakage detection, were evaluated and compared based on classification performance using confusion matrix and performance indices. The results show that the latest boosting models, XGBoost, GBM, LightGBM, and CatBoost, yield superior classification performance compared to RF and AdaBoost models. Although the performance of the latest boosting models is similar, the LightGBM model (accuracy = 0.960, precision = 0.941, recall = 0.955, F1-score = 0.948, and specificity = 0.970) shows the best performance. Therefore, the latest boosting machine learning models can be used as effective predicting tools to develop big data-based water pipeline leakage detection systems and manage water pipeline leak risks.
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