Knowing the pressure at all times in each node of a water distribution system (WDS) facilitates safe and efficient operation. Yet, complete measurement data cannot be collected due to the limited number of instruments in a real-life WDS. The data-driven methodology of reconstructing all the nodal pressures by observing only a limited number of nodes is presented in the paper. The reconstruction method is based on K-localized spectral graph filters, wherewith graph convolution on water networks is possible. The effect of the number of layers, layer depth and the degree of the Chebyshev-polynomial applied in the kernel is discussed taking into account the peculiarities of the application. In addition, a weighting method is shown, wherewith information on friction loss can be embed into the spectral graph filters through the adjacency matrix. The performance of the proposed model is presented on 3 WDSs at different number of nodes observed compared to the total number of nodes. The weighted connections prove no benefit over the binary connections, but the proposed model reconstructs the nodal pressure with at most 5% relative error on average at an observation ratio of 5% at least. The results are achieved with shallow graph neural networks by following the considerations discussed in the paper.
Reconstructing GRACE Terrestrial Water Storage with Spatio-Temporal Graph Neural Networks: An Application to South America
HafenCity University, Computational Methods Lab（哈芬城大学计算方法实验室）；HafenCity University, Geodesy and Adjustment Theory（哈芬城大学大地测量与平差理论系）；GFZ Helmholtz Centre for Geosciences, Global Geomonitoring and Gravity Field（亥姆霍兹地球科学研究中心全球地球监测与重力场部）
Nodal Hydraulic Head Estimation through Unscented Kalman Filter for Data-driven Leak Localization in Water Networks
Institut de Robòtica i Informàtica Industrial, CSIC-UPC（西班牙国家研究委员会-加泰罗尼亚理工大学工业机器人与信息技术研究所）；University of Bucharest（布加勒斯特大学）；Institute for Logic and Data Science（逻辑与数据科学研究所）；Politehnica University of Bucharest（布加勒斯特理工大学）；Supervision, Safety and Automatic Control Research Center (CS2AC) - UPC（加泰罗尼亚理工大学监督、安全与自动控制研究中心）
University of Rochester（罗切斯特大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
Recent advances in neuroimaging along with algorithmic innovations in statistical learning from network data offer a unique pathway to integrate brain structure and function, and thus facilitate revealing some of the brain's organizing principles at the system level. In this direction, we develop a supervised graph representation learning framework to model the relationship between brain structural connectivity (SC) and functional connectivity (FC) via a graph encoder-decoder system, where the SC is used as input to predict empirical FC. A trainable graph convolutional encoder captures direct …
Budapest University of Technology and Economics（布达佩斯技术与经济大学）
Real-time control of pumps can be an infeasible task in water distribution systems (WDSs) because the calculation to find the optimal pump speeds is resource-intensive. The computational need cannot be lowered even with the capabilities of smart water networks when conventional optimization techniques are used. Deep reinforcement learning (DRL) is presented here as a controller of pumps in two WDSs. An agent based on a dueling deep q-network is trained to maintain the pump speeds based on instantaneous nodal pressure data. General optimization techniques (e.g., Nelder-Mead method, differential…
Dual Unscented Kalman Filter Architecture for Sensor Fusion in Water Networks Leak Localization
Institut de Robòtica i Informàtica Industrial, CSIC-UPC（西班牙科学研究理事会-加泰罗尼亚理工大学机器人与工业自动化研究所）；LOS-CS-FMI, University of Bucharest（布加勒斯特大学数学与计算机科学学院计算机科学系）；Supervision, Safety and Automatic Control Research Center (CS2AC) of the Universitat Politècnica de Catalunya（加泰罗尼亚理工大学监督、安全与自动控制研究中心）；Politehnica University of Bucharest（布加勒斯特理工大学）
Scan2LoD3: Reconstructing semantic 3D building models at LoD3 using ray casting and Bayesian networks
Technical University of Munich（慕尼黑工业大学）；University of Oxford（牛津大学）；Bruno Kessler Foundation（布鲁诺·凯斯勒基金会）；Munich University of Applied Sciences（慕尼黑应用技术大学）
