pySIMDEUM - An open-source stochastic water demand end-use model

[EN] Water demand is a crucial input parameter in water distribution system analysis because it can fluctuate over various temporal and spatial scales. In the past, researchers developed stochastic models that can provide realistic consumption patterns for simulations to account for those demand dynamics. Parameters for stochastic models are usually retrieved by fitting these models on smart water meter data. The stochastic demand model SIMDEUM uses an entirely different approach by generating highly realistic water demands based on (country-specific) statistical information only, without the need for measurements. While this approach makes SIMDEUM widely applicable in the water sector, its widespread usage within the community has been hindered due to its software implementation and availability. We produced pySIMDEUM, an open-source and object-oriented implementation of the SIMDEUM software in the popular and freely available programming language Python. The pySIMDEUM software package is not only publicly available for usage within the water field — it is also intended to build the cornerstone of a widespread pySIMDEUM community of active developers. We want to use the WDSA/CCWI conference to address interested researchers or practitioners in the water sector and invite them to contribute to the software package as active part of the pySIMDEUM community. We will show SIMDEUM’s history and past applications, the mathematical approach behind SIMDEUM and pySIMDEUM, where to download and install the pySIMDEUM package, the structure of the program, and a minimal example of how easily pySIMDEUM can be used to generate realistic stochastic water demand patterns from scratch. Furthermore, we will highlight possible future applications of the new pySIMDEUM tool. These applications include automatic parametrisation of pySIMDEUM parameters on smart meter data, coupling stochastic demands directly with hydraulic solvers, or how to enable city-scale stochastic demand simulations.Steffelbauer, D.; Hillebrand, B.; Blokker, M. (2024). pySIMDEUM - An open-source stochastic water demand end-use model. Editorial Universitat Politècnica de València. https://doi.org/10.4995/WDSA-CCWI2022.2022.1477

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

The Shape of Water Distribution Systems - Describing local structures of water networks via graphlet analysis

[EN] The performance, vulnerability, and resilience of water distribution systems (WDS) are connected to its underlying topological structure (aka its shape). The literature mostly differentiates between two main shapes of networks - branched or looped. However, real networks come in various shapes and forms spanning between the two extremes of purely branched and looped types. Although these networks are globally topologically different, they may show high similarity on the local scale of a borough or a neighbourhood, or vice versa. Recent studies focused on describing WDS via graph theory representing pipes as edges and customers, tanks, and reservoirs as nodes, for example. The first attempt of graph theoretical applications showed promising results in estimating the global resilience of WDS, but there is a limited number of metrics that take the importance of local topology into consideration. Furthermore, iterative estimation of local vulnerability by simulating faults in each element of the system is prohibitively expensive from a computational point of view (i.e., various hydraulic simulations for assessing the vulnerability of each part of the system are needed).This research enters the new terrain of local WDS investigations through graphlet analysis. Graphlets are small connected subgraphs of a large network and have recently gathered much attention as a useful concept to describe local topology and uncover structural design principles of complex networks. Consequently, these novel analyses techniques can provide deep insights into how local WDS structures influence their overall performance.In this work, we investigate the influence of local network structures on the resilience of entire networks through graphlet analysis. First, we calculate local vulnerability of the network elements and global resilience indicators (i.e. Todini index, pipe and node criticality indices). We additionally simulate fault scenarios with EPANET and evaluate volumes of unsupplied demand on a multitude of real WDS. Second, we investigate the graphlet substructure of those WDSs to assess how much of the network’s vulnerability can be described by purely looking at the topology. First results show that graphlet representation of local neighbourhoods can serve as an efficient proxy metric capable of replacing computationally heavy performance analysis based on extensive hydraulic simulation. We additionally compare the influence of local changes on subgraphs to show how local changes in network design may grant improved robustness against such failures, ultimately increasing global resilience based on changing local topology. Urban water management can benefit from the proposed approach by not only identifying the most vulnerable elements of the critical infrastructure but providing insight into how to build globally more resilient WDS networks by enforcing small and therefore economical topological changes.Kerimov, B.; Tscheikner-Gratl, F.; Taormina, R.; Steffelbauer, D. (2024). The Shape of Water Distribution Systems - Describing local structures of water networks via graphlet analysis. Editorial Universitat Politècnica de València. https://doi.org/10.4995/WDSA-CCWI2022.2022.1478

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)