Contamination source identification based on sequential Bayesian approach for water distribution network with stochastic demands
提出一种实时序贯贝叶斯方法，利用传感器报警数据计算各污染源的后验概率，通过排序识别污染源，并采用分层树结构避免组合爆炸，适用于随机需水量下的供水管网。
Efficient identification of the source of contamination in a water distribution network is crucial to the safe operation of the system. In this article, we propose a real-time sequential Bayesian approach to deal with this problem. Simulations are conducted to simulate hydraulic information and the propagation of contamination in the network. Sensor alarms are recorded in multiple simulations to establish the observation probability distribution function. Then this information is used to compute the posterior probability of each possible source for the observed alarm pattern in real time. Finally, the contamination source is identified based on a ranking of the posterior probability. The key contribution of this work is that the probability distributions for all possible observations are organized into a concise hierarchical tree structure and the challenge of combinatorial explosion is avoided. Furthermore, a variation analysis of the posterior probability is conducted to give significance probability to the obtained identification result. The effectiveness of this method is verified by a case study with a realistic water distribution network.