A Decision Tree Approach to the Risk Evaluation of Urban Water Distribution Network Pipes

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.