Civil and Environmental Engineering, Technion - IIT, Haifa 32000, Israel.
Environ Sci Technol. 2012 Aug 7;46(15):8212-9. doi: 10.1021/es3014024. Epub 2012 Jul 11.
In this study, a general framework integrating a data-driven estimation model with sequential probability updating is suggested for detecting quality faults in water distribution systems from multivariate water quality time series. The method utilizes artificial neural networks (ANNs) for studying the interplay between multivariate water quality parameters and detecting possible outliers. The analysis is followed by updating the probability of an event, initially assumed rare, by recursively applying Bayes' rule. The model is assessed through correlation coefficient (R(2)), mean squared error (MSE), confusion matrices, receiver operating characteristic (ROC) curves, and true and false positive rates (TPR and FPR). The product of the suggested methodology consists of alarms indicating a possible contamination event based on single and multiple water quality parameters. The methodology was developed and tested on real data attained from a water utility.
在这项研究中，提出了一种将数据驱动的估计模型与顺序概率更新相结合的通用框架，用于从多元水质时间序列中检测供水中的质量故障。该方法利用人工神经网络（ANNs）研究多元水质参数之间的相互作用，并检测可能的异常值。分析之后，通过递归应用贝叶斯法则来更新初始假设为罕见事件的概率。该模型通过相关系数（R(2)）、均方误差（MSE）、混淆矩阵、接收者操作特征（ROC）曲线以及真阳性率和假阳性率（TPR 和 FPR）进行评估。该方法的产物是基于单个和多个水质参数的警报，指示可能的污染事件。该方法是在从供水公司获得的实际数据上开发和测试的。
