基于深度学习框架的时空联合供水管网漏损检测研究

Combined spatial-temporal leakage detection within water distribution system based on deep learning framework

以深度学习框架为基础,提出了一种时空联合供水管网漏损检测模型。该模型首先运用Node2Vec算法求解不同时间段内节点特征;其次,通过模糊C-均值聚类法,利用管网模型节点特征进行分区。最后,以不同时间段的压力敏感度作为输入,漏损位置的分区号作为标签,通过深度信念神经网络进行训练,并通过训练后的模型对管网漏损位置进行检测。在实例分析中,以A市实际供水管网拓扑结构进行验证,利用MATLAB-Open Water Analytics toolbox联合编程建模,结果表明,各个时间段的检测效果均较优,正确率均达到为80%以上。因此,该模型能够有效地检测管网漏损。

A spatial-temporal leakage detection model was presented based on deep learning framework.Firstly,nodes in water distribution system were divided into different clusters in terms of the topological structure via Fuzzy c-mean(FCM)algorithm.Then,the embeddings of SCADA nodes were solved by Node2Vec algorithm.Finally,the deep belief network(DBN)was employed to train a model that leakage condition was regarded as label while embedding was input.The leakage was detected by the trained model.In the case study,MATLAB and Open Water Analytics toolbox were applied to code the spatial-temporal leakage detection model.The results showed that accuracies were all up to 8o%.Therefore,the model had a great effect on the leakage detection.