Water utilities and municipalities must embrace technological innovation to address the exacerbating challenges and uncertainties posed by climate change, urbanization, and population growth. The progressive digitalization of urban water infrastructure and the adoption of IoT solutions for water resources are opening new opportunities for the design, planning, and management of more sustainable and resilient urban water networks and systems. At the same time, the “digital water” revolution is strengthening the interconnection between urban water systems and other critical infrastructure sectors (e.g., energy grids, transportation networks) motivating the development of novel approaches accounting for the intrinsic complexity of such coupled systems. This session aims to provide an active forum to discuss and exchange knowledge on state-of-the-art and emerging tools, framework, and methodologies for planning and management of modern urban water infrastructure, with a focus on digitalization and/or interconnection with other systems. Topics and applications could belong to any area of urban water demand and supply network analysis, modelling and management, including intelligent sensors and advanced metering, digital twins, novel applications of IoT for urban water, and challenges to their implementation or risk of lock-in of rigid system designs. Additional topics may include big-data analytics and information retrieval, data-driven behavioural analysis, descriptive and predictive models of water demand, experimental approaches to demand management, water demand and supply optimization, trend and anomaly (e.g., leak) identification. Examples of interesting investigations on interconnected systems include cyber-physical security of urban water systems (i.e., communication infrastructure), combined reliability studies on power-to-water networks (energy), and minimization of impacts of flooding on urban networks.
vPICO presentations: Tue, 27 Apr
The well-established Water Energy Food Nexus, meaning the investigation of the three subsystems as a whole complex of interlinkages and trade-offs has been a revolutionary step towards holistic system thinking. More Nexuses have occurred and have been studied through the last decades, expanding the central WEF triplet. Sim4Nexus has acknowledged the importance of expanding the triplet to a quintet, also including the land uses and the climate change as core components, towards a holistic analysis that targets to climate change mitigation and adaptation actions and resource efficiency. In this work, we present the methodological framework developed and tested on multiple case studies of various particularities and scales. We present an assessment workflow of numerous steps and iterations that includes, among others, the conceptual and bio-physical modelling of the quintet synergies and trade-offs, the exploitation of existing databases and thematic models, the stakeholder mapping and engagement, the policy analysis, the formulation of narratives, the pre-nexus assessment, the workshops, the System Dynamic Modeling, the Science-Policy interface, and the Serious Gaming. The aim of this work is to transfer the experience and lessons learnt of a demanding, trans-disciplinary and intersectoral work and provide a roadmap for future Nexus—not necessarily the specific quintet—analysis efforts.
How to cite: Laspidou, C., Kofinas, D., and Ramos, E. P.: Resource Nexus Methodological Framework: Making the Nexus Operational, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3932, https://doi.org/10.5194/egusphere-egu21-3932, 2021.
In the field of urban water infrastructure (UWI), a system-wide management, that interacts with other infrastructure areas, is gaining importance. Additionally, new technologies based on the Internet of Things (IoT) concept, as part of smart city development, are rapidly emerging. This allows for new possibilities in the management of UWI. However, in literature, there is a lack of information about usable communication technology making it challenging to reproduce existing research.
To contribute to this ongoing development, the results of a detailed literature review are presented focusing on (smart) applications related to network-based UWI. Therefore, existing, and new applications of urban drainage and water distribution - including nature-based solutions - are analysed to provide a comprehensive analysis over required spatial and temporal resolution of measurement and control data for a cross-system view. To close the knowledge gap between UWI applications and usable (IoT) communication technology, a detailed framework is presented to identify suitable communication technologies based on spatial and temporal requirements for smart UWI operation. This framework can be used by researchers and stakeholders to choose a suitable communication technology based on their intended UWI applications, or vice versa, to indicate UWI applications, which are possible with an existing communication network.
The intersections with basic conditions of data communication (e.g., transmission range, data rate, reliability) reveal, that small-scale system parts (e.g., IoT-based micro storages, nature-based solutions) can be integrated into an overall controlled system by using state-of-the-art IoT technology in combination with citizen participation. However, monitoring and controlling networks in the field of UWI are based on battery-powered measurement devices due to underground and remote installation sites, and requires investment costs, too. Consequently, a balance between measurement equipment and effectiveness of the implemented applications is required to achieve economic and ecological sustainability
How to cite: Oberascher, M., Rauch, W., and Sitzenfrei, R.: Digitalisation and citizen participation - opportunities for integrated management of urban water infrastructure, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2062, https://doi.org/10.5194/egusphere-egu21-2062, 2021.
Water utilities across the globe are facing numerous challenges to their operations and management posed by ageing infrastructure, urbanization, and climate change. Specific challenges include severe floods and droughts, changing urban water demands, costs related to the maintenance of infrastructure systems, and increasingly critical conditions for wastewater overflow in combined sewer systems. Recent developments of digital technologies offer opportunities to address these challenges. Smart monitoring and automatic control, advanced analytics, informed demand management, and digital customer engagement open new paths to more efficient water use, better understanding of resource availability and quality, or faster detection of failures and anomalies. Although many utilities have started the process of digital transformation, few of them are on the same track. The variety of possible approaches to digitalisation raises the following questions: How is digital transformation impacting the water utility sector? What are the drivers and challenges for such transformation? What are the key enabling technologies?
To address these questions, the online “Smart Water Survey” (http://smartwatersurvey.com) was designed to analyse common priorities, best practices and technologies, and challenges entailed by the digital transformation process in the water utility sector. The survey maps out a water utility’s operating network and company structure as divided into five different subsections: (1) water supply & drinking water treatment, (2) water distribution network & operating systems, (3) wastewater & rainwater management, (4) customers & demand management, and (5) data warehouse & IT systems. For each subsection, different aspects of the digital transformation are investigated. Besides providing a general overview on the ongoing digitalisation trends and its determinants, the answers obtained from over 60 utilities worldwide allow assessing the digital maturity of water utilities and deriving recommendations on future digital developments. Independent sections and targeted questions in the survey are organized in a way to overcome the potential information bias due to the utility’s perception of digitalisation and subjective evaluation of its importance.
While the survey will remain open for future updates, the authors have chosen to report the current results available at the end of 2020. The results indicate that most of the participating utilities have already taken on digitalisation and are moving forward by adopting new digital technologies, regardless of their geographic origin, company age, and size. However, differences are apparent among the five subsections mentioned above. For subsection (2), in more than 50% of the cases the digital technology in question was already implemented, while for (4) this number was roughly 30%. Additionally, in 50% of the cases in (4) technology was either being implemented or planned in the near future and not considered in 20% of the cases. As the driving elements for their transition, utilities reported economic factors as most influential across all subsections with a ratio of 66%. Governmental influence and ecologic factors had a comparably smaller influence with a ratio 26% and 8%, respectively.
How to cite: Daniel, I., Ajami, N., Castelletti, A., Savic, D., Stewart, R., Becker, M., and Cominola, A.: How is digital transformation impacting the water utility sector? - Insights from a worldwide online utility survey, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12540, https://doi.org/10.5194/egusphere-egu21-12540, 2021.
Digitalization is claimed to revolutionize water utilities in many urban areas across the world, contributing to urban water security. It involves the use of Information Technology, Data Analysis and Electronic Monitoring in urban water governance with significant improvements in quality water services and customer satisfaction. However, a holistic success story where digitalization of each and every urban water process and service, is found only in a handful of cities. The challenge in most cases is not the availability of an appropriate digital technology but the implementation of the technology. In our study, we try to find the constraints faced in the implementation processes by assessing the required enabling conditions of digitalization as well as the outcomes. Two cities, Singapore and Bengaluru as case studies were selected to compare the implementation process of Smart Water Meters, Supervisory Control and Data Acquisition (SCADA) and Flood Early Warning. Singapore is a developed city in terms of water with an excellent water management system that provides remarkable water-related services. On the other hand, Bengaluru, a rapidly growing city in India and known for its information technology and digital industry, has embarked on the path towards digitalization in water. The comparison of the implementation of these three technologies provides interesting insights that we have extended to generalized inferences about the implementation of digitalization in urban water. We have found that the enabling conditions such as the existence of enabling technologies, management capacity and conducive policy framework are crucial for implementation. Interestingly, outlining the target problems that digitalization is expected to address is equally important for achieving favourable outcomes. The inferences developed in this study will help the adoption of digital technology by urban water utilities, especially in the developing world and in turn strengthen water security.
How to cite: Banerjee, C., Saraswat, C., and Bhaduri, A.: Comparing digitalization of urban water processes and services in Singapore and Bengaluru, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14081, https://doi.org/10.5194/egusphere-egu21-14081, 2021.
Dealing with uncertainty in infrastructure planning is a challenge. Planning decisions need to be made in face of unknown future condition, and, in the meantime, it is essential that they are flexible enough to be adapted as new information unfolds. This indeed is important for multi-sector decision making where the complexity of the interconnected system and the uncertainty thereof hinders the modelling and analysis. Multistage stochastic optimisation provides a mechanism to incorporate these two attributes into planning decisions. However, its expensive computation as well as the appropriateness of its sequential decisions beyond the first few stages reduce its implementability. We introduce `Decision Rule' as a way to approximate the multistage problem, where the decisions at each stage are functions of the system complexity and the future uncertainty. We introduce a family of linear, polynomial, conditional if-then based rules and show how they approximate the multistage stochastic problem. We investigate their implications for urban water demand and supply network planning problem. Further we discuss some state-of-the-art and emerging tools for increasing the accuracy of the rules.
How to cite: Erfani, T. and Harou, J. J.: Rule-based modelling for adaptive complex infrastructure planning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15651, https://doi.org/10.5194/egusphere-egu21-15651, 2021.
Water management has recently explored data-driven models to improve the adaptability of Water Distribution Systems (WDS) and strengthen decision making under uncertain conditions. The focus on these tools is motivated by the increasing availability of information and their proven performance in other fields. Modeling WDS with these techniques has been demonstrated to be useful; however, the traditional machine learning tools do not account for the graph structure present in the WDS. Considering this essential information offers the possibility to increase performance and to help the learning process. In this work, we introduce Graph Neural Networks (GNNs) for modeling WDS. GNNs are processing architectures to perform neural network tasks for data related to a graph. We first present the definitions and interpretations for using this framework in water networks. Then we compare the GNN approach against standard neural networks to predict an overall resilience metric in a benchmark system. The benefits of including the network structure in the learning process by the GNN are shown in the analysis of the obtained results.
How to cite: Garzón, A., Bentivoglio, R., Isufi, E., Kapelan, Z., and Taormina, R.: Modeling Water Distribution Systems with Graph Neural Networks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9378, https://doi.org/10.5194/egusphere-egu21-9378, 2021.
A water distribution network (WDN) is a critical and life-line infrastructure that transports and distributes water, an essential resource for human life, to local communities. A WDN is often modeled as a two-dimensional complex network consisting of vertices (nodes) and pipes (edges), and it has both characteristics of lattice-like and tree-like structures. With these characteristics, Son et al. (2021) proposed an approach to identify an optimal grid ratio in terms of functionality - efficiency and vulnerability - of a WDN using the lattice to tree network model (LTNM). Their result showed that the grid ratio of a real WDN is often significantly lower than the optimal value, which means that the function of the WDN can be improved by increasing the grid ratio. However, as the range of functions varies depending on where grids are located at a fixed grid ratio, simply adding pipes without considering their location does not incur a linear increase in system function. Therefore, it is important to identify the critical locations to add pipes where the functions of the system are most improved. In addition, it is necessary to determine if adding pipes is possible or not since pipe installation is not allowed for some places. In this study, we (1) identify possible spots where pipes can be added, (2) rank the identified spots where pipes are added regarding the extent of increments of function, and (3) propose an optimal (or a suboptimal) design with an optimally increased with grid ratio by adding pipes to the ranked locations in order. The results indicate that, the performance of WDNs which originally had low grid ratios are improved by adding pipes at reliable spots. The proposed approach illustrates how the structure and function of existing WDNs can be developed by modifying the proportion of grids.
Acknowledgments: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2019R1C1C1008017).
How to cite: Lee, H., Shin, H., and Park, J.: Positioning grids at critical locations for the design of water distribution network, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13835, https://doi.org/10.5194/egusphere-egu21-13835, 2021.
Municipal drinking water distribution networks are complex and dynamic systems often spanning many hundreds of kilometers and serving thousands of consumers. Degradation of water quality within a distribution network can be associated to water age (i.e., time elapsed after treatment). Norwegian distribution networks often consist of an intricate combination of pressure zones, in which the transport path(s) between source and consumer is not easily ascertained. Water age is therefore poorly understood in many Norwegian distribution networks. In this study, simulations obtained from a water network model were used to estimate water age in a Norwegian municipal distribution network. A full-scale tracer study using sodium chloride salt was conducted to assess simulation accuracy. Water conductivity provided empirical estimates of salt arrival time at five monitoring stations. These estimates were consistently higher than simulated peak arrival times. Nevertheless, empirical and simulated water age correlated well, indicating that additional network model calibration will improve accuracy. Subsequently, simulated mean water age also correlated strongly with heterotrophic plate count (HPC) monitoring data from the distribution network (Pearson’s R= 0.78, P= 0.00046), indicating biomass accumulation during distribution—perhaps due to bacterial growth or biofilm interactions—and illustrating the importance of water age for water quality. This study demonstrates that Norwegian network models can be calibrated with simple and cost-effective salt tracer studies to improve water age estimates. Improved water age estimation will increase our understanding of water quality dynamics in distribution networks. This can, through digital tools, be used to monitor and control water age, and its impact on biogrowth in the network.
How to cite: Rakstang, J. K., B. Waak, M., M. Rokstad, M., and Hallé, C.: Improving water age estimation in a drinking water distribution network by field study and digital modeling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14741, https://doi.org/10.5194/egusphere-egu21-14741, 2021.
As more and more computational power becomes available at increasingly affordable prices, the last years have seen a veritable explosion in the number of sensors and interconnected devices. This evolution is well known and often referred to as the 4th industrial revolution, or the IoT. The water sector, albeit often conservative in adopting new technologies, will profit from this continued digitalisation in various ways.
In this work we focus on the vision of covering entire sewer systems by tightly knit sensor networks which can process the generated amount of data simultaneously. Given the large number of sensors required, the only possibility to implement such a network is keeping costs as low as possible for the individual devices or use already existing sensors in multiple ways (e.g., traffic cameras helping in flood detection).
Using hardware of the Raspberry Pi ecosystem, currently retailing at less than 100$, we collected continuous video footage of an artificial open channel in a laboratory setting and used a deep neural network to extract the water level and surface velocity. The measurement accuracy of the prediction algorithm was then compared to conventional flow sensors to assess the practicality of this approach. Preliminary results in a laboratory setting indicate a sufficient prediction accuracy of the water level for engineering uses but further work is needed to verify this in a long-term field study.
After this initial stage, deploying the sensor in a real-world setting as part of the B-WaterSmart project is planned. Apart from verifying the results under real conditions, we will then be able to assess the long-term behaviour of this approach. This includes an evaluation of the maintenance effort. As the sensor is not in direct contact with the sewage, the typical need for frequent cleaning should be greatly reduced, which in turn is expected to further lower the costs.
We argue that if such a cheap sensor can ultimately be established as a viable alternative to more conventional flow sensors, the vision of sewer networks covered entirely by sensors, could become more attainable in practice.
How to cite: Meier, R., Tscheikner-Gratl, F., and Makropoulos, C.: Spending Less Than 100$ on Real-Time Sewer Flow Measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15635, https://doi.org/10.5194/egusphere-egu21-15635, 2021.
The H2020 innovation project digital-water.city (DWC) aims at boosting the integrated management of water systems in five major European cities – Berlin, Copenhagen, Milan, Paris and Sofia – by leveraging the potential of data and digital technologies. The goal is to quantify the benefits of a panel of 15 innovative digital solutions and achieve their long-term uptake and successful integration in the existing digital systems and governance processes. One of these promising technologies is a new generation of sensors for measuring combined sewer overflow occurrence, developed by ICRA and IoTsens.
Recent EU regulations have correctly identified CSOs as an important source of contamination and promote appropriate monitoring of all CSO structures in order to control and avoid the detrimental effects on receiving waters. Traditionally there has been a lack of reliable data on the occurrence of CSOs, with the main limitations being: i) the high number of CSO structures per municipality or catchment and ii) the high cost of the flow-monitoring equipment available on the market to measure CSO events. These two factors and the technical constraints of accessing and installing monitoring equipment in some CSO structures have delayed the implementation of extensive monitoring of CSOs. As a result, utilities lack information about the behaviour of the network and potential impacts on the local water bodies.
The new sensor technology developed by ICRA and IoTsens provides a simple yet robust method for CSO detection based on the deployment of a network of innovative low-cost temperature sensors. The technology reduces CAPEX and OPEX for CSO monitoring, compared to classical flow or water level measurements, and allows utilities to monitor their network extensively. The sensors are installed at the overflows crest and measure air temperature during dry-weather conditions and water temperature when the overflow crest is submerged in case of a CSO event. A CSO event and its duration can be detected by a shift in observed temperature, thanks to the temperature difference between the air and the water phase. Artificial intelligence algorithms further help to convert the continuous measurements into binary information on CSO occurrence. The sensors can quantify the CSO occurrence and duration and remotely provide real-time overflow information through LoRaWAN/2G communication protocols.
The solution is being deployed since October 2020 in the cities of Sofia, Bulgaria, and Berlin, Germany, with 10 offline sensors installed in each city to improve knowledge on CSO emissions. Further 36 (Sofia) and 9 (Berlin) online sensors will follow this winter. Besides its main goal of improving knowledge on CSO emissions, data in Sofia will also be used to identify suspected dry-weather overflows due to blockages. In Berlin, data will be used to improve the accuracy of an existing hydrodynamic sewer model for resilience analysis, flood forecasting and efficient investment in stormwater management measures. First results show a good detection accuracy of CSO events with the offline version of the technology. As measurements are ongoing and further sensors will be added, an enhanced set of results will be presented at the conference.
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How to cite: Riechel, M., Gutierrez, O., Busquets, S., Amela, N., Dimova, V., Gunkel, M., Caradot, N., and Rouault, P.: A network of low-cost temperature sensors for real-time monitoring of combined sewer overflow, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16386, https://doi.org/10.5194/egusphere-egu21-16386, 2021.
Water demand predictions forms an integral part of sustainable management practices for water supply systems. Demand prediction models aides in water system maintenance, expansions, daily operational planning and in the development of an efficient decision support system based on predictive analytics. In recent years, it has also found wide application in real-time control and operation of water systems as well. However, short term water demand forecasting is a challenging problem owing to the frequent variations present in the urban water demand patterns. There are numerous methods available in literature that deals with water demand forecasting. These methods can be roughly classified into statistical and machine learning methods. The application of deep learning methods for forecasting water demands is an upcoming research area that has found immense traction due to its ability to provide accurate and scalable models. But there are only a few works which compare and review these methods when applied to a water demand dataset. Hence, the main objective of this work is the application of different commonly used deep learning methods for development of a short-term water demand forecast model for a real-world dataset. The algorithms studied in this work are (i) Multi-Layer Perceptron (MLP) (ii) Gated Recurrent Unit (GRU) (iii) Long Short-Term Memory (LSTM) (iv) Convolutional Neural Networks (CNN) and (v) the hybrid algorithm CNN-LSTM. Optimal supervised learning framework required for forecasting the one day ahead water demand for the study area is also identified. The dataset used in this study is from Hillsborough County, Florida, US. The water demand data was available for a duration of 10 months and the data frequency is about once per hour. These algorithms were evaluated based on the (1) Mean Absolute Percentage Error (MAPE) and (ii) Root Mean Squared Error (RMSE) values. Visual comparison of the predicted and true demand plots was also employed to check the prediction accuracy. It was observed that, the RMSE and MAPE values were minimal for the supervised learning framework that used the previous 24-hour data as input. Also, with respect to the forecast accuracy, CNN-LSTM performed better than the other methods for demand forecast, followed by MLP. MAPE values for the developed deep learning models ranged from 5% to 25%. The quantity, frequency and quality of data was also found to have substantial impact on the accuracy of the forecast models developed. In the CNN-LSTM based forecast model, the CNN component was found to effectively extract the inherent characteristics of historical water consumption data such as the trend and seasonality, while the LSTM part was able to reflect on the long-term historical process and future trend. Thus, its water demand prediction accuracy was improved compared to the other methods such as GRU, MLP, CNN and LSTM.
How to cite: G Rajakumar, A., Anthony, A., and Kumar, V.: Application of deep learning methods for urban water demand forecast modelling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9572, https://doi.org/10.5194/egusphere-egu21-9572, 2021.
The touristic factor has not been adequately addressed in urban water demand studies, so far, and it is often neglected also by the water utilities. In a beach resort, where the water demand drastically increases during the summer months, a better understanding of the touristic uses would substantially contribute to improving the management of the available water supplies, already scarce during the dry season.
The present study contributes to the quantification of the water demand in touristic activities, analysing the single-consumer volumes of more than a thousand accommodation facilities (hotels and apartment hotels) and two hundred bathing establishments in Rimini, the most important coastal destination in Italy. For each user, the estimated monthly water volumes have been collected and validated, and the seasonal patterns analysed for an observation period of twelve years.
Each hotel and bathing establishment was characterised identifying the main attributes that may drive the water demand. The consumer volumes are put into relation with the hotel size, identified by the number of rooms, and the influence of the hotel category and of the presence of water-demanding services (such as swimming pool or spa, garden to be irrigated, restaurant) is analysed. For the bathing establishments, the number of beach umbrellas identify the number of expected costumers and some specific services are used as additional features to interpret the variability of the water consumptions.
The analysis identifies the features that have more influence on the water consumption patterns for hotels and bathing establishments. This kind of study allows to infer the behaviour of similar users, in order to estimate the expected patterns, as a function of the specific attributes of the touristic activity. Such benchmarks would also allow to check if the actual consumer volumes are in line with the typical ones, highlighting possible malfunctioning of the metering system or anomalous consumptions that would prompt an in-depth analysis of the water-uses in the hotel or bathing establishment premises.
How to cite: Toth, E., Bragalli, C., and Neri, M.: Characterization of hotels and bathing establishments water uses for understanding urban demand in touristic cities, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13470, https://doi.org/10.5194/egusphere-egu21-13470, 2021.
Collecting and managing high temporal resolution (< 1 minute) residential water use data is challenging due to cost and technical requirements associated with the volume and velocity of data collected. It is well known that this type of data has potential to expand our knowledge of residential water use, inform future water use predictions, and improve water conservation strategies. However, most studies collecting this type of data have been focused on the practical application of the data (e.g., developing and applying end use disaggregation algorithms) with much less focus on how the data were collected, retrieved, quality controlled, and managed to enable data visualization and analysis. We developed an open-source, modular, generalized cyberinfrastructure system to automate the process from data collection to analysis. The system has three main architectural components: first, the sensors and dataloggers for water use monitoring; second, the data communication, parsing and archival tools; and third, the analyses, visualization and presentations of data produced for different audiences. For the first component, we present a low-cost datalogging device, designed for installation on top of existing, analog, magnetically driven, positive displacement, residential water meters that can collect data at a user configurable time resolution interval. The second component consists of a system developed using existing open-source software technologies that manages the data collected, including services and databasing. The final element includes software tools for retrieving the data that can be integrated with advanced data analytics tools. The system was used in a single family residential water use data collection case study to test the scalability and performance of its functionalities within our design constraints. Testing with a base system configuration, our results show that the system requires approximately six minutes to process a single day of data collected at a four second temporal resolution for 500 properties. Thus, the system proved to be effective beyond the typical number of participants observed in similar studies of residential water use and would scale well beyond this even with the modest system resources we used for testing. All elements of the cyberinfrastructure developed are freely available in open source repositories for re-use.
How to cite: Bastidas Pacheco, C. J., Horsburgh, J. S., Brewer, J. C., Tracy, R. J., and Caraballo, J.: Advancing open source cyberinfrastructure for collecting, processing, storing and accessing high temporal resolution residential water use data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6031, https://doi.org/10.5194/egusphere-egu21-6031, 2021.
Water end use disaggregation aims to separate household water consumption data collected from a single residential water meter into appliance/fixture-level consumption data. In recent years, the field has rapidly expanded as the value of disaggregated data has been shown for understanding water use behavior, identifying anomalies, and identifying opportunities for conserving water. Several methods have been developed for disaggregating water end uses from high temporal resolution water use data collected using residential smart water meters. However, most existing methods have been incorporated into proprietary software tools and have been tested using datasets that are inaccessible due to privacy issues, with the result being that neither the code nor the data from these studies are available for verification or potential reuse. We describe and demonstrate a new, open source, and reproducible water end use disaggregation and classification tool that builds upon the results of existing smart water metering and end use disaggregation studies. The tool was designed and developed in Python and can be accessed via any current Python programming environment. It was tested on anonymized, high temporal resolution datasets collected from 31 residential dwellings located in the Cities of Logan and Providence, Utah, USA for a period of one month. Results from different meter types and sizes were tested to demonstrate the accuracy and reproducibility of the tool in disaggregating and classifying high temporal resolution data into individual water end use events. Execution of the tool requires approximately one minute for processing one-day of data collected at a four second time interval for one dwelling. The disaggregation tool is open source and can be adapted to specific research needs. The anonymized dataset we used to develop and test the tool is openly available in the HydroShare data repository.
How to cite: Attallah, N., Horsburgh, J., and Bastidas Pacheco, C.: Advancing the cyberinfrastructure for smart water metering: A new open source water end use disaggregation algorithm, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3347, https://doi.org/10.5194/egusphere-egu21-3347, 2021.
Our modern urban environment relies on critical infrastructures that serve vital societal functions, such as water supply and sanitation, which are exposed to various threats of both physical and cyber nature. Despite the progress in protection and increased vigilance, long-established practices within the water utilities may rely on precarious methods for the characterization and assessment of threats, with uncertainty pertaining to risk-relevant data and information. Sources for uncertainty can be attributed to e.g. limited capabilities of deterministic approaches, siloed analysis of water systems, use of ambiguous measures to describe and prioritise risks or common security misconceptions. To tackle those challenges, this work brings together an ensemble of solutions, to form a novel, unified process of resilience assessment for the water sector against an emerging cyber-physical threat landscape e.g., cyber-attacks on the command and control sub-system. Specifically, the proposed framework sets out an operational workflow that combines, inter alia, a) an Agent-Based Modelling (ABM) approach to derive alternative routes to quantify risks considering the dynamics of socio-technical systems, b) an adaptable optimisation platform which integrates advanced multi-objective algorithms for system calibration, uncertainty propagation analysis and asset criticality prioritization and c) a dynamic risk reduction knowledge-base (RRKB) designed to facilitate the identification and selection of suitable risk reduction measures (RRM). This scheme is overarched by a cyber-physical testbed, able to realistically model the interactions between the information layer (sensors, PLCs, SCADA) and the water distribution network. The testbed is designed to assess the water system beyond normal operational capacity. It facilitates the exploration of emergent and unidentified threats and vulnerabilities leading to Low Probability, High Consequence (LPHC) events that systems are not originally designed to handle. It also evaluates alternative risk treatment options against case-appropriate indicators. The final product is the accretion of actionable information to integrate risk into decision-making in a practical and standardized form. Our work envisions to bring forth state-of-art technologies and approaches for the cyber-wise water sector. We aspire to enhance existing capabilities for large utilities and enable small and medium water utilities with typically less resources, to reinforce their systems’ resilience and be better prepared against cyber-physical and other threats.
How to cite: Moraitis, G., Nikolopoulos, D., Koutiva, I., Tsoukalas, I., Karavokiros, G., and Makropoulos, C.: The PROCRUSTES testbed: tackling cyber-physical risk for water systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14903, https://doi.org/10.5194/egusphere-egu21-14903, 2021.
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