The Use of Cluster Analysis to Evaluate the Impact of COVID-19 Pandemic on Daily Water Demand Patterns

Enhancing long-term water quality modeling by addressing base demand, demand patterns, and temperature uncertainty using unsupervised machine learning techniques

Water quality modelling in Water Distribution systems (WDS) is frequently affected by uncertainties in input variables such as base demand and decay constants. When utilizing simulation tools like EPANET, which necessitate exact numerical inputs, these uncertainties can result in inaccurate simulations. This study proposes a novel framework that leverages unsupervised machine learning, specifically a Gaussian Mixture Model (GMMs), to represent and integrate these uncertainties in the simulation. By classifying historical water demand into fuzzy clusters, the framework allows for certain linguistic inputs (e.g., "high" or "low" demand) to be used in water quality simulations. The framework also incorporates representative hourly demand patterns and temperature-dependent chlorine decay constants based on historical data correlations. Validations were conducted on the Anytown network using WNTR-EPANET, comparing simulated chlorine residuals with Validation data from 181 steady-state simulations. The simulation through the framework achieved a Jensen-Shannon Divergence (JSD) of <0.008 across all demand clusters, indicating high similarity between predicted and actual probability distributions . In comparison to other simulation scenarios tested, which exhibited increased variability (JSD > 0.18), the proposed framework demonstrated improved accuracy in representing chlorine residual distributions. The methodology is adaptable to other systems, if similar historical datasets containing key variables, such as flow rates and temperature, are provided. While the framework offers a more flexible and accurate approach to handling uncertainties in WDS, its effectiveness is contingent upon the availability of robust historical demand and temperature data for decay constant calibration.

The impact of the number of high temporal resolution water meters on the determinism of water consumption in a district metered area

Developments in data mining techniques have significantly influenced the progress of Intelligent Water Systems (IWSs). Learning about the hydraulic conditions enables the development of increasingly reliable predictive models of water consumption. The non-stationary, non-linear, and inherent stochasticity of water consumption data at the level of a single water meter means that the characteristics of its determinism remain impossible to observe and their burden of randomness creates interpretive difficulties. A deterministic model of water consumption was developed based on data from high temporal resolution water meters. Seven machine learning algorithms were used and compared to build predictive models. In addition, an attempt was made to estimate how many water meters data are needed for the model to bear the hallmarks of determinism. The most accurate model was obtained using Support Vector Regression (8.9%) and the determinism of the model was achieved using time series from eleven water meters of multi-family buildings.

Application of "Behind the Barriers" Model at Neighbourhood Scale to Improve Water Management under Multi-Risks Scenarios: A Case Study in Lyon, France

In modern urban areas, water management highly depends on the socio-ecological urban water cycle (UWC) that heavily relies on water infrastructures. However, increasing water-related hazards, natural and/or human-based, makes it difficult to balance water resources in the socio-ecological UWC. In the last decade, urban infrastructure resilience has rapidly become a popular topic in disaster risk management and inspired many studies and operational approaches. Among these theories and methods, the "Behind the Barriers" model (BB model), developed by Barroca and Serre in 2013, is considered a theory that allows effective and comprehensive analysis of urban infrastructure resilience through cognitive, functional, correlative, and organisational dimensions. Moreover, this analysis can be a reference to develop actions that improve infrastructure resilience under critical scenarios. Therefore, this study aims to study resilience design actions based on the BB model to achieve socio-ecological water balance and assess the performance of these actions. The study focuses on water management on a neighbourhood scale, which is considered the essential urban unit to study and improve the resilience of critical infrastructures, such as water services. The Part-Dieu neighbourhood in Lyon, France is selected as a case study, and it highlights the need to develop indicators to assess the performance of implemented actions in a structural and global resilience framework, to understand urban systems as complex and dynamic systems to provide decision support, and to strengthen crisis prevention and management perspectives in a dynamic approach.

Estimation of household water consumption pattern in a metropolitan area taking the impact of the COVID-19 pandemic

A new approach for estimating the household water consumption pattern was developed by taking the impact of the COVID-19 pandemic using geographical data. Water consumption data for two years before and a year after the outbreak of the pandemic were analyzed to recognize the consumption pattern on annual and bi-monthly time scales as well as in different spatial classes. Following the recognition of the pattern, the spatiotemporal distribution of household water consumption was estimated based on the discovered connections between consumption and geographical variables. Once a regression relationship between consumption and population density was observed, an idea was developed to investigate the linear equations and their coefficient of parameters in water consumption groups from very low to very high classes using the training data. The coefficients were then adjusted to account for the pandemic's impact on the consumption pattern. Results showed that the highest increases in consumption were 11% for May-July due to the impact of the pandemic while the impact was from decreasing type during lockdowns. A pandemic-induced decline in the mean of consumption was linked to temporary migration by high-income families, whereas the water consumption of others faced an increase. The impact has also increased the slope of the linear relationship between the annual water consumption and population density increased by 3.5%. The proposed model estimated the annual water consumption with the accuracy of %3.77, %1.82, and %1.85 for two years before, one year before and one year after the pandemic, respectively.

Water demand profile before and during COVID-19 pandemic in a Brazilian social housing complex

The COVID-19 pandemic has changed the way resources are consumed around the world. The relationship between the pandemic and water consumption has important implications for the management of water use and must be evaluated in depth. The main goal of this research paper is to establish a comparison between pre-pandemic and pandemic water consumption profiles for 14 social-housing buildings located in Joinville, Southern Brazil. Telemetry data from each apartment were collected on an hourly basis before and during the COVID-19 pandemic. The analysis was based on descriptive statistics on the hourly and daily water consumption in addition to its profile plots. The best probability distribution fitting was also determined. To assess the differences in water consumption due to de pandemic, the Wilcoxon-Mann-Whitney test was employed and a Generalized Linear Model with mixed effects was fitted to the data. The Lognormal distribution was shown to be the most appropriate to model the water consumption data. Due to the COVID-19 pandemic, the two daily peak consumption periods changed from 12 h to 15 h and from 19 h to 21 h. The COVID-19 pandemic also impacted daily water consumption, leading to a small, yet significant, increase in demand in the first quarter of the pandemic period.

Development of cluster analysis methodology for identification of model rainfall hyetographs and its application at an urban precipitation field scale

Despite growing access to precipitation time series records at a high temporal scale, in hydrology, and particularly urban hydrology, engineers still design and model drainage systems using scenarios of rainfall temporal distributions predefined by means of model hyetographs. This creates the need for the availability of credible statistical methods for the development and verification of already locally applied model hyetographs. The methodology development for identification of similar rainfall models is also important from the point of view of systems controlling stormwater runoff structure in real time, particularly those based on artificial intelligence. This paper presents a complete methodology of division of storm rainfalls sets into rainfalls clusters with similar temporal distributions, allowing for the final identification of local model hyetographs clusters. The methodology is based on cluster analysis, including the hierarchical agglomeration method and k-means clustering. The innovativeness of the postulated methodology involves: the objectivization of clusters determination number based on the analysis of total within sum of squares (wss) and the Caliński and Harabasz Index (CHIndex), verification of the internal coherence and external isolation of clusters based on the bootmean parameter, and the designated clusters profiling. The methodology is demonstrated at a scale of a large urban precipitation field of Kraków city on a total set of 1806 storm rainfalls from 25 rain gauges. The obtained results confirm the usefulness and repeatability of the developed methodology regarding storm rainfall clusters division, and identification of model hyetographs in particular clusters, at a scale of an entire city. The applied methodology can be successfully transferred on a global scale and applied in large urban agglomerations around the world.

Advanced Techniques for Monitoring and Management of Urban Water Infrastructures—An Overview

Water supply systems are essential for a modern society. This article presents an overview of the latest research related to information and communication technology systems for water resource monitoring, control and management. The main objective of our review is to show how emerging technologies offer support for smart administration of water infrastructures. The paper covers research results related to smart cities, smart water monitoring, big data, data analysis and decision support. Our evaluation reveals that there are many possible solutions generated through combinations of advanced methods. Emerging technologies open new possibilities for including new functionalities such as social involvement in water resource management. This review offers support for researchers in the area of water monitoring and management to identify useful models and technologies for designing better solutions.

Evaluation of Istanbul from the environmental components' perspective: what has changed during the pandemic?

This study aims to determine the 1-year change over the pandemic period in Istanbul, the megacity with the highest population in Turkey, based on environmental components. Among the environmental topics, water consumption habits, changes in air quality, changes due to noise elements, and most importantly, the changes in usage habits of disposable plastic materials that directly affect health have been revealed. The results obtained showed that, in Istanbul, 8.1 × 10⁸ gloves should be considered waste, and considering the population living in districts along coastal areas, the number of waste masks that are likely to end up in the sea was 325.648 pieces/day. The results of the air quality and noise measurements during the pandemic showed that reductions in parallel with human activities were recorded with the lockdown effect. The average noise values of the districts along both sides of the Bosporus, where urbanization is concentrated, were between 50 and 59 dB. The precautions taken during the pandemic have had an effective role in reducing air pollution in Istanbul. In the measurements, the parameters with effective reductions were PM₁₀ (7-47%), PM_2.5 (13-48%), NO₂ (13-38%), and SO₂ (10-56%). As a result, Istanbul's year of changes during the pandemic period, in terms of water, air, noise, and solid plastic wastes, which are the most important components of the environment, is presented.

Development of Methods for Diagnosing the Operating Conditions of Water Supply Networks over the Last Two Decades

The need for water supply companies to exercise control over the operating conditions of water supply networks has contributed to the development of a number of methods for their diagnosis. The diagnostics of technical infrastructure is a constantly developing field, and therefore, over the years, we have observed the development of various methods of diagnosing network operating conditions and their classification. The article presents, in a synthetic way, the division and review of the previously used methods of diagnosing the operating conditions of water supply networks. The authors also classified and assessed the usefulness of the methods of diagnosis in specific operating conditions. The review carried out by the authors shows that there is a need for research on the detection of operating conditions of water supply networks under the operating conditions of real systems. The results of our deep analysis allow for the understanding of the most important areas of research, as well as the existing worldwide trends in the development of methods for leak diagnosis and detection in water distribution networks. This review is a compendium of knowledge on the detection and diagnosis of water supply networks.

Reshaping sustainable development trajectory due to COVID-19 pandemic

Humanity could face the COVID-19 epidemic to crystallize a sustainable future for the water, hygiene, and food sectors. The epidemic has affected the sustainability of water, food, and health institutions in Egypt. Water consumption levels have increased in the agricultural sector to ensure food security. Regular handwashing is one of the most important measures to prevent the epidemic, and this has an impact on water consumption. The purpose of the research is to reshape sustainable development trajectory due to COVID-19 pandemic in Egypt through three interdependent phases: the first is devoted to forecast how the pandemic could be spreading in Egypt, the second is assigned to foresee implications and consequences of the pandemic on water, food, and human activities, and the third is dedicated to exploring how Egypt could utilize non-conventional water resources as a precious resource to fight the pandemic and explore sustainable recovery strategies. The results could be summarized as the diffusion of COVID-19 pandemic may be considered a group of evolutionary processes. The vision of growth to a limit may be applied; the number of cases of COVID-19 grows rapidly, but the growth will be reduced due to negative feedback signals from the environment. The paper concludes that the COVID-19 epidemic could be addressed by enhancing the water sector to better cope with future shocks. Water, food, health, and work opportunities could be provided and managed sustainably. The need to provide water to wash the hands of all citizens has been emphasized to fight the coronavirus. Non-conventional water resources could be an engine to ambitious plans to drive economic growth through megaprojects. Egypt would enable transforming this crisis into an opportunity to accelerate the pace of action towards achieving Sustainable Development Goals.