Estimating irregular water demands with physics-informed machine learning to inform leakage detection

Leakages in drinking water distribution networks pose significant challenges to water utilities, leading to infrastructure failure, operational disruptions, environmental hazards, property damage, and economic losses. The timely identification and accurate localisation of such leakages is paramount for utilities to mitigate these unwanted effects. However, implementation of algorithms for leakage detection is limited in practice by requirements of either hydraulic models or large amounts of training data. Physics-informed machine learning can utilise hydraulic information thereby circumventing both limitations. In this work, we present a physics-informed machine learning algorithm that analyses pressure data and therefrom estimates unknown irregular water demands via a fully connected neural network, ultimately leveraging the Bernoulli equation and effectively linearising the leakage detection problem. Our algorithm is tested on data from the L-Town benchmark network, and results indicate a good capability for estimating most irregular demands, with R2 larger than 0.8. Identification results for leakages under the presence of irregular demands could be improved by a factor of 5.3 for abrupt leaks and a factor of 3.0 for incipient leaks when compared the results disregarding irregular demands.Comment: submitted to Water Research on July 17th, 202

A convex optimization approach for automated water and energy end use disaggregation

A detailed knowledge of water consumption at an end-use level is an essential requirement to design and evaluate the efficiency of water saving policies. In the last years, this has led to the development of automated tools to disaggregate high resolution water consumption data at the household level into end use categories. In this work, a new disaggregation algorithm is presented. The proposed algorithm is based on the assumption that the disaggregated signals to be identified are piecewise constant over the time and it exploits the information on the time-of-day probability in which a specific water use event might occur. The disaggregation problem is formulated as a convex optimization problem, whose solution can be efficiently computed through numerical solvers. Specifically, the disaggregation problem is treated as a least-square error minimization problem, with an additional (convex) penalty term aiming at enforcing the disaggregate signals to be piece-wise constant over the time. The proposed disaggregation algorithm has been initially tested against household electricity data available in the literature. The obtained results look promising and similar results are expected to be obtained for water data

Benefits and challenges of using smart meters for advancing residential water demand modeling and management: a review

Over the last two decades, water smart metering programs have been launched in a number of medium to large cities worldwide to nearly continuously monitor water consumption at the single household level. The availability of data at such very high spatial and temporal resolution advanced the ability in characterizing, modeling, and, ultimately, designing user-oriented residential water demand management strategies. Research to date has been focusing on one or more of these aspects but with limited integration between the specialized methodologies developed so far. This manuscript is the first comprehensive review of the literature in this quickly evolving water research domain. The paper contributes a general framework for the classification of residential water demand modeling studies, which allows revising consolidated approaches, describing emerging trends, and identifying potential future developments. In particular, the future challenges posed by growing population demands, constrained sources of water supply and climate change impacts are expected to require more and more integrated procedures for effectively supporting residential water demand modeling and management in several countries across the world

Profiling residential water users’ routines by eigenbehavior modelling

Developing effective demand-side management strategies is essential to meet future residential water demands, pursue water conservation, and reduce the costs for water utilities. The effectiveness of water demand management strategies relies on our understanding of water consumers’ behavior and their consumption habits and routines, which can be monitored through the deployment of smart metering technologies and the adoption of data analytics and machine learning techniques. This work contributes a novel modeling procedure, based on a combination of clustering and principal component analysis, which allows performing water users’ segmentation on the basis of their eigenbehaviors (i.e., recurrent water consumption behaviors) automatically identified from smart metered consumption data. The approach is tested against a dataset of smart metered water consumption data from 175 households in the municipality of Tegna (CH). Numerical results demonstrate the potential of the method for identifying typical profiles of water consumption, which constitute essential information to support residential water demand management

Developing a stochastic simulation model for the generation of residential water end-use demand time series

: Smart metering technologies allow for gathering high resolution water demand data in the residential sector, opening up new opportunities for the development of models describing water consumers’ behaviors. Yet, gathering such accurate water demand data at the end-use level is limited by metering intrusiveness, costs, and privacy issues. In this paper, we contribute a stochastic simulation model for synthetically generating high-resolution time series of water use at the end-use level. Each water end-use fixture in our model is characterized by its signature (i.e., its typical single-use pattern), as well as frequency distributions of its number of uses per day, single use duration, time of use during the day, and contribution to the total household water demand. The model relies on statistical data from a real-world metering campaign across 9 cities in the US. Showcasing our model outputs, we demonstrate the potential usability of this model for characterizing the water end-use demands of different communities, as well as for analyzing the major components of peak demand and performing scenario analysis

Modelling residential water consumers’ behaviors by feature selection and feature weighting

Identifying the most relevant determinants of water consuming or saving behaviors at the household level is key to building mathematical models that predict urban water demand variability in space and time and to explore the effects of different Water Demand Management Strategies for the residential sector. This work contributes a novel approach based on feature selection and feature weighting to model the single-user consumption behavior at the household level. A two-step procedure consisting of the extraction of the most relevant determinants of users’ consumption and the identification of a predictive model of water consumers’ profile is proposed and tested on a real case study. Results show the effectiveness of the proposed method in capturing the influence of candidate determinants on residential water consumption, as well as in attaining sufficiently accurate predictions of users’ consumption profiles, which constitutes essential information to support residential water demand management

Post-pandemic intended use of remote teaching and digital learning media in higher education. Insights from a europe-wide online survey

The COVID-19 pandemic has had a transformational and potentially long-lasting impact on higher education institutions, with the rapid shift to “Emergency Remote Education”. Two years after the begin of the pandemic, institutions are either returning to presence formats with different speed or converging towards hybrid formats, begging the question what remains of the newly acquired skills and experience with remote teaching and digital learning media? Here, we present the findings of the first European-Union-wide survey on the potential longterm impacts of COVID-19 on higher education, evaluating over 800 responses from students and faculty members of higher education institutions located in 17 different European countries. Our survey – developed in the context of the ide3a university alliance (http://ide3a.net/) highlights possible differences between students and instructors in their attitude toward retaining digital teaching formats and media, examines which formats have increased in use over the course of the pandemic, and investigates which of them are intended to be kept and consolidated post-pandemic. The tools and formats examined in this survey include tools for communication and collaboration, formats of didactic activity, as well as assessment formats. Survey responses reveal that all evaluated tools and format have significantly increased in use during the pandemic and most of them are intended to be used at lower frequency in the future, while still at significantly higher frequency than before the pandemic. Moreover, attitudes toward long-term use of remote teaching and digital learning media seems to be comparable between students and faculty members, except regarding some tool

How effectively (or not) can science and research be turned into adopted solutions and policies?

How to create an impact on policies, operations, and society across the interdisciplinary sectors in which we - as researchers - are involved? Managing the Water-Energy-Food-Ecosystem (WEFE) nexus and pursuing climate resilience is the core task of several European (EU) projects and is in the highest interests of our society. The European Commission’s research funding programs attempt to address a large range of topics and offer unique opportunities for scientists to create a tangible impact on the environment and society. We are currently involved in different EU projects, including AWESOME (PRIMA), which aims at managing the WEFE nexus across sectors and scales in the South Mediterranean exploring innovative technologies such as soilless agriculture in the Nile Delta; CLINT (H2020), which is developing Machine Learning (ML) techniques to improve climate science in the detection, causation, and attribution of extreme events to advance climate services; IMPETUS (H2020), whose efforts are dedicated on the elaboration of climate data space enhanced with ML algorithms to support the elaboration of climate policies; REACT4MED (PRIMA), which focuses on combating land degradation and desertification by improving sustainable land and water management through the identification of local good restoration practices and their potential upscaling; Gaza H2.0: Innovation and water efficiency (EuropeAid), which aims at promoting efficient and sustainable water supply and demand as well as knowledge transfer to enhance resilience against water scarcity in Gaza; GoNEXUS (H2020), which is developing an evaluation framework to design and assess innovative solutions for an efficient and sustainable coordinated governance of the WEFE nexus; NexusNet (COST), which creates the network and the community of WEF nexus advocates for a low-carbon economy in Europe and beyond; NEXOGENESIS (H2020), which focuses on streamlining water-related policies with artificial intelligence and reinforcement learning; MAGO (PRIMA), which builds web applications for water and agriculture in the Mediterranean; BIONEXT (HEU), which is interlinked with the Intergovernmental Panel on Biodiversity and Ecosystem Services and aims at creating transformative change through nexus analysis. Despite the efforts of the scientific community, there is still a gap between research and practice. Researchers face difficulties in engaging stakeholders and decision-makers to jointly explore and shape the developed solutions, as well as to truly adopt them. The large-scale implementation of suitable technological solutions might require time and financial resources beyond the project’s lifetime and capacity. The lack of follow-ups and collaboration among projects with similar aims can be some of the reasons lying behind. Also, the complexity of finding open data in data-scarce regions makes results less trustable in the eyes of international agencies, while the pressure of publishing often turns research tasks into pure academic exercises. To what extent does the European strategy work? Is it only gaining scientific advances or also leading to local policy changes? Engaging important local actors (e.g., ministries), small-medium enterprises and societal members in the project consortia, empowering scientists by ensuring feedback loops with local governmental agencies, including the human dimension into modelling, and running effective capacity-building campaigns can be some food for thoughts to shape new strategies