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.
Combined spatial-temporal leakage detection within water distribution system based on deep learning framework
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 leak-age 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 80％.Therefore,the model had a great effect on the leakage detection.
万方数据
维普
