Abstract
To evaluate the risk of a pipe in the water supply network of Beijing, we used the accident records of the gridding urban management (GUM) system. In addition, road and building information derived from a three-dimensional (3D) electronic map was also employed. A machine learning algorithm, the decision tree, was employed to train and evaluate the dataset. The results show that the contributions of the surrounding buildings and roads are neglectable, except for super-high-rise buildings, which have limited contributions. This finding is consistent with the results of other studies. The decision tree identifies dominant features and isolates the risk contribution of such features. The output tree structure indicated that the time since the last accident is a dominant factor, to which super-high-rise buildings contribute slightly. A cut-off value of 0.019 was chosen to predict high-risk regions. Approximately 0.4% of the data were predicted to be high risk, and the corresponding gain in risk rate was approximately 19.2. This model may be used in cities where detailed profiles of water supply pipes and maintenance records are not available or are expensive to achieve.
Highlights
Water supply is an important part of the urban lifeline in a city
We introduced the amplification ratio of risk (ARR) to evaluate the model
Model of the decision tree shows that time is the dominant factor in determining the risk
Summary
Prevention and Control Action Plan was initiated by the State Council in April 2015. This plan is known as the Water Ten Plan (WTP). The WTP proposed several measures and set out long-term objectives at a national level [1], and the renovation and upgradation of aging water pipes in distribution networks was initiated. These pipes have been in service for more than 50 years and are mostly made of outdated materials. A long-term goal for the national average leakage rate in water distribution networks was proposed. It is worth noting that the national average water loss rate was 14.32% in 2015 [2]
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