OpenSDM - An Open Sensor Data Management Tool

Exchange of scientific data and metadata between single users or organizations is a challenging task due to differences in data formats, the genesis of data collection, ontologies and prior knowledge of the users. Different data storage requirements, mostly defined by the structure, size and access scenarios, require also different data storage solutions, since there is no and there cannot be a data format which is suitable for all tasks and needs that occur especially in a scientific workflow. Besides data, the generation and handling of additional corresponding metadata leads us to the additional challenge of defining the meaning of data, which should be formulated in a way that it can be commonly understood to get out a maximum of expected and shareable information of the observed processes. In our domain we are able to take advantage of standards defined by the Open Geospatial Consortium, namely the standards defined by the Sensor Web Enablement, WaterML and CF-NetCDF working groups. Even though these standards are freely available and some of them are commonly used in specialized software packages, the adaption in widespread end-user software solutions still seems to be in its beginnings. This contribution describes a software solution developed at Graz University of Technology, which targets the storage and exchange of measurement data with a special focus on meteorological, water quantity and water quality observation data collected within the last three decades. The solution was planned on basis of long-term experience in sewer monitoring and was built on top of open-source software only. It allows high-performance storage of time series and associated metadata, access-controlled web services for programmatic access, validation tasks, event detection, automated alerting and notification. An additional web-based graphical user interface was created which gives full control to end-users. The OpenSDM software approach makes it easier for measurement station operators, maintainers and end-users to take advantage of the standards of the Open Geospatial Consortium, which usage should be promoted in the water related communities

Leakage Localization In Virtual District Metered Areas With Differential Evolution

Leakages in water distribution systems (WDS) can lead to supply interruptions, contaminations and economic losses. Hence finding leaks before they cause severe problems is a crucial task for water utilities. To identify the existence of leaks, night flow measurements in district-metered areas (DMA) are common practice. Therefore, the entire system has to be subdivided in hydraulically separated partial networks. However, many utilities do not want to lose the hydraulic redundancy of their system and hence search for other solutions to identify and allocate leaks. In our research, the effects of leakages on the hydraulic behaviour of WDS are utilized to find the optimal solution for placing hydraulic sensors. From the discrepancy of the unperturbed and the perturbed WDS due to the occurrence of leakage, a methodology is developed which enables an efficient placement of flow meters and pressure sensors. This is achieved by a Fault Sensitivity Matrix (FSM). Finding the optimal position of a minimum number of sensors is carried out by a specific Genetic Algorithm called Differential Evolution (DE). DE is chosen due to its good rate of convergence reducing the computation time. This is of special interest for large WDS. Once an optimal sensor placement is obtained, DE is also used for leakage localization. The methodology has been applied and tested in two different WDS. The first WDS was a model network published by Poulakis in 2003. The second was a partial network of an Austrian city. Here the task was to place as few sensors as possible concerning economical costs while guaranteeing leakage localization in an area of a predefined size. In this paper it is shown that DE performs well, both on sensor placement and leakage localization, for both investigated systems. Additionally the implementation of demand and measurement uncertainties is outlined

Computational Efficient Small Signal Model For Fast Hydraulic Simulations

To increase the performance, quality and reliability of water distribution systems, implementing efficient computational and algorithmic techniques, has become a major tasks in hydraulic modelling. Examples can be found in online condition monitoring, real time control applications, or model based leakage detection and location approaches, etc. All these techniques require extensive hydraulic simulations. Well known and trusted hydraulic simulation tools like EPANET, etc. are deployed within the individual task specific code using provided interface routines. The flow dependent friction models of hydraulic systems require an iterative solution strategy to solve the problem. Although this is done efficiently using Newton-Raphson methods, the simulation output provided by those tools is limited to raw information (i.e. flow and head). Yet the superior algorithms often require more information than the raw output. I.e. gradient based optimization methods rely on derivative information. In this paper we report on a hydraulic small signal model which can be directly derived from the output of the hydraulic simulation tool itself. The model provides cheap computational access to internal information like gradients, sensitivity, etc. of the hydraulic simulation. The ATCA equilibrium structure of the model is numerically suitable and provides properties like a positive definite stiffness matrix enabling the efficient use of direct solvers like Cholesky decomposition. Further, the symmetry provides the property of self adjointness which enables the efficient use of Greens functions. We will present how the model can be assembled from the raw simulator output and present how to use it as linear approximation, for the computation of search directions in gradient based optimization schemes, for sensitivity analysis, as well as for the computation of covariance propagation due to uncertain demands

Sensor Placement and Leakage Localization considering Demand Uncertainties

AbstractDetecting and locating leaks in water distribution systems is of great interest. For the localization of leaks we make use of pressure sensors alongside a calibrated hydraulic EPANET model of the investigated system. Leakage localization is solved with a Differential Evolution algorithm. For sensor placement we use a non-binarized leak sensitivity matrix with a projection-based leak isolation approach. Additionally, the effect of uncertain hydraulic model parameters on the measurement quantities is investigated by Monte Carlo simulations and was incorporated in the sensor placement algorithm. Uncertainty analysis, sensor placement and leakage location was tested on two hydraulic systems

Flow Measurements Derived from Camera Footage Using an Open-Source Ecosystem

Sensors used for wastewater flow measurements need to be robust and are, consequently, expensive pieces of hardware that must be maintained regularly to function correctly in the hazardous environment of sewers. Remote sensing can remedy these issues, as the lack of direct contact between sensor and sewage reduces the hardware demands and need for maintenance. This paper utilizes off-the-shelf cameras and machine learning algorithms to estimate the discharge in open sewer channels. We use convolutional neural networks to extract the water level and surface velocity from camera images directly, without the need for artificial markers in the sewage stream. Under optimal conditions, our method estimates the water level with an accuracy of ±2.48% and the surface velocity with an accuracy of ±2.08% in a laboratory setting—a performance comparable to other state-of-the-art solutions (e.g., in situ measurements)

Assessing the performances and transferability of graph neural network metamodels for water distribution systems

Metamodels accurately reproduce the output of physics-based hydraulic models with a significant reduction in simulation times. They are widely employed in water distribution system (WDS) analysis since they enable computationally expensive applications in the design, control, and optimisation of water networks. Recent machine-learning-based metamodels grant improved fidelity and speed; however, they are only applicable to the water network they were trained on. To address this issue, we investigate graph neural networks (GNNs) as metamodels for WDSs. GNNs leverage the networked structure of WDS by learning shared coefficients and thus offering the potential of transferability. This work evaluates the suitability of GNNs as metamodels for estimating nodal pressures in steady-state EPANET simulations. We first compare the effectiveness of GNN metamodels against multi-layer perceptrons (MLPs) on several benchmark WDSs. Then, we explore the transferability of GNNs by training them concurrently on multiple WDSs. For each configuration, we calculate model accuracy and speedups with respect to the original numerical model. GNNs perform similarly to MLPs in terms of accuracy and take longer to execute but may still provide substantial speedup. Our preliminary results indicate that GNNs can learn shared representations across networks, although assessing the feasibility of truly general metamodels requires further work.Sanitary EngineeringMultimedia Computin

Investigating the characteristics of residential end uses of water: A worldwide review

A detailed characterization of residential water consumption is essential for ensuring urban water systems' capability to cope with changing water resources availability and water demands induced by growing population, urbanization, and climate change. Several studies have been conducted in the last decades to investigate the characteristics of residential water consumption with data at a sufficiently fine temporal resolution for grasping individual end uses of water. In this paper, we systematically review 114 studies to provide a comprehensive overview of the state-of-the-art research about water consumption at the end-use level. Specifically, we contribute with: (1) an in-depth discussion of the most relevant findings of each study, highlighting which water end-use characteristics were so far prioritized for investigation in different case studies and water demand modelling and management studies from around the world; and (2) a multi-level analysis to qualitatively and quantitatively compare the most common results available in the literature, i.e. daily per capita end-use water consumption, end-use parameter average values and statistical distributions, end-use daily profiles, end-use determinants, and considerations about efficiency and diffusion of water-saving end uses. Our findings can support water utilities, consumers, and researchers (1) in understanding which key aspects of water end uses were primarily investigated in the last decades; and (2) in exploring their main features considering different geographical, cultural, and socio-economic regions of the world.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Sanitary Engineerin