Scientific Results

This catalogue is obtained by conducting a systematic literature review of scientific studies and reviews related to monitoring, forecasting, and simulating the inland water cycle. The analysis maps scientific expertise across research groups and classifies findings by the type of inland water studied, application focus, and geographical scope. A gap analysis will identify missing research areas and assess their relevance to policymaking.

ID ▲	Type	Year	Authors	Title	Venue/Journal	DOI	Abstract
publications-4021	article	2017	Wang, Chao and Wang, Chao and Wang, Chao and Zhou, Shiyu and Zhou, Shiyu	Contamination source identification based on sequential Bayesian approach for water distribution network with stochastic demands		10.1080/24725854.2017.1315782	Efficient identification of the source of contamination in a water distribution network is crucial to the safe operation of the system. In this article, we propose a real-time sequential Bayesian approach to deal with this problem. Simulations are conducted to simulate hydraulic information and the propagation of contamination in the network. Sensor alarms are recorded in multiple simulations to establish the observation probability distribution function. Then this information is used to compute the posterior probability of each possible source for the observed alarm pattern in real time. Finally, the contamination source is identified based on a ranking of the posterior probability. The key contribution of this work is that the probability distributions for all possible observations are organized into a concise hierarchical tree structure and the challenge of combinatorial explosion is avoided. Furthermore, a variation analysis of the posterior probability is conducted to give significance probability to the obtained identification result. The effectiveness of this method is verified by a case study with a realistic water distribution network.
publications-4022	article	2017	Klise, Katherine A. and Klise, Katherine A. and Bynum, Michael and Bynum, Michael and Moriarty, Dylan and Moriarty, Dylan Michael and Murray, Regan and Murray, Regan	A SOFTWARE FRAMEWORK FOR ASSESSING THE RESILIENCE OF DRINKING WATER SYSTEMS TO DISASTERS WITH AN EXAMPLE EARTHQUAKE CASE STUDY.	Environmental Modelling and Software	10.1016/j.envsoft.2017.06.022	Water utilities are vulnerable to a wide variety of human-caused and natural disasters. The Water Network Tool for Resilience (WNTR) is a new open source Pythonβ„Ά package designed to help water utilities investigate resilience of water distribution systems to hazards and evaluate resilience-enhancing actions. In this paper, the WNTR modeling framework is presented and a case study is described that uses WNTR to simulate the effects of an earthquake on a water distribution system. The case study illustrates that the severity of damage is not only a function of system integrity and earthquake magnitude, but also of the available resources and repair strategies used to return the system to normal operating conditions. While earthquakes are particularly concerning since buried water distribution pipelines are highly susceptible to damage, the software framework can be applied to other types of hazards, including power outages and contamination incidents.
publications-4023	article	2017	Kanakoudis, Vasilis and Kanakoudis, Vasilis and Kanakoudis, Vasilis and Tsitsifli, Stavroula	Potable water security assessment β€“ a review on monitoring, modelling and optimization techniques, applied to water distribution networks	Desalination and Water Treatment	10.5004/dwt.2017.21784
publications-4024	article	2018	Sela, Lina and Amin, Saurabh	Robust sensor placement for pipeline monitoring: Mixed integer and greedy optimization	Advanced Engineering Informatics	10.1016/j.aei.2018.02.004
publications-4025	article	2018	Sankary, Nathan and Ostfeld, Avi	Multiobjective Optimization of Inline Mobile and Fixed Wireless Sensor Networks under Conditions of Demand Uncertainty	Journal of Water Resources Planning and Management	10.1061/(asce)wr.1943-5452.0000930	AbstractUsing a system to promptly detect anomalous water quality levels in a water distribution system (WDS) is a critical task to ensure security of a public water supply. Using continuous monito...
publications-4026	article	2019	Arnon, Tehila Asheri and Arnon, Tehila Asheri and Ezra, Shai and Ezra, Shai and Fishbain, Barak and Fishbain, Barak	Water characterization and early contamination detection in highly varying stochastic background water, based on Machine Learning methodology for processing real-time UV-Spectrophotometry.	Water Research	10.1016/j.watres.2019.02.027
publications-4027	article	2019	Koutiva, Ifigeneia and Koutiva, Ifigeneia and Makropoulos, Christos and Makropoulos, Christos	Exploring the Effects of Alternative Water Demand Management Strategies Using an Agent-Based Model	Water	10.3390/w11112216	Integrated urban water management calls for tools that can analyze and simulate the complete cycle including the physical, technical, and social dimensions. Scientific advances created simulation tools able to simulate the urban water cycle as realistically as possible. However, even these tools cannot effectively simulate the social component and quantify how behaviors are shaped by external stress factors, such as climate and policies. In this work, an agent-based modeling tool, urban water agentsβ€™ behavior (UWAB) is used to simulate the water demand behavior of households and how it is influenced by water demand management strategies and drought conditions. UWAB was applied in Athens, Greece to explore the effect of different water demand management strategies to the reliability of the Athens hydrosystem. The results illustrate the usability of UWAB to support decision makers in identifying how β€_x009c_strictβ€_x009d_ water demand management measures are needed and when and for how long to deploy them in order to alleviate potential water supply issues.
publications-4028	article	2019	Sun, Li and Sun, Lian and Yan, Hexiang and Yan, Hexiang and Xin, Kunlun and Xin, Kunlun and Tao, Tao and Tao, Tao	Contamination source identification in water distribution networks using convolutional neural network	Environmental Science and Pollution Research	10.1007/s11356-019-06755-x	Contamination source identification (CSI) is significant for water quality security and social stability when a contamination intrusion event occurs in water distribution systems (WDSs). However, in research, this is an extremely challenging task for many reasons, such as limited number of water quality sensors and their limitations in detecting contaminants. Hence, some researchers have introduced consumersβ€™ complaint information as an alternative of sensors for CSI. But the problem with this approach is that the uncertainty of complaint delay time has a great impact on the identification accuracy. To address this issue, this study constructed complaint matrices to present the spatiotemporal characteristics of consumer complaints in an intrusion event and proposed a new methodology employing convolution neural network (CNN)β€”a deep learning algorithmβ€”for the purpose of pattern recognition. CNN aimed to explore the inherent characteristics of complaint patterns corresponding to different contaminant intrusion nodes and to improve the performance of identifying the contamination source based on consumer complaint information. Two case studies illustrated methodology effectiveness in WDSs of various scales, even with the high uncertainties of complaint delay time. The comparison between CNN and a back-propagation artificial neural network algorithm demonstrates that the former framework possesses stronger robustness and higher accuracy for CSI.
publications-4029	article	2020	de Lima e Silva, PetrΓ΄nio CΓΆndido and de Lima e Silva, PetrΓ΄nio CΓΆndido and do Carmo Batista, Paulo Vitor and Batista, Paulo V. C. and Lima, HΓ©lder Seixas and Lima, Helder Seixas and Lima, Helder Seixas and Lima, Helder Seixas and Alves, Marcos AntΓ΄nio and Alves, Marcos Antonio and GuimarΓ£es, Frederico Gadelha and GuimarΓ£es, Frederico G. and Silva, Rodrigo C. P. and Silva, Rodrigo C. P.	COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions	Chaos Solitons & Fractals	10.1016/j.chaos.2020.110088	Abstract The COVID-19 pandemic due to the SARS-CoV-2 coronavirus has directly impacted the public health and economy worldwide. To overcome this problem, countries have adopted different policies and non-pharmaceutical interventions for controlling the spread of the virus. This paper proposes the COVID-ABS, a new SEIR (Susceptible-Exposed-Infected-Recovered) agent-based model that aims to simulate the pandemic dynamics using a society of agents emulating people, business and government. Seven different scenarios of social distancing interventions were analyzed, with varying epidemiological and economic effects: (1) do nothing, (2) lockdown, (3) conditional lockdown, (4) vertical isolation, (5) partial isolation, (6) use of face masks, and (7) use of face masks together with 50\% of adhesion to social isolation. In the impossibility of implementing scenarios with lockdown, which present the lowest number of deaths and highest impact on the economy, scenarios combining the use of face masks and partial isolation can be the more realistic for implementation in terms of social cooperation. The COVID-ABS model was implemented in Python programming language, with source code publicly available. The model can be easily extended to other societies by changing the input parameters, as well as allowing the creation of a multitude of other scenarios. Therefore, it is a useful tool to assist politicians and health authorities to plan their actions against the COVID-19 epidemic.
publications-4030	article	2020	Maziarz, Mariusz and Maziarz, Mariusz and Zach, Martin and Zach, Martin	Agent-based modelling for SARS-CoV-2 epidemic prediction and intervention assessment: A methodological appraisal.	Journal of Evaluation in Clinical Practice	10.1111/jep.13459	BACKGROUND: Our purpose is to assess epidemiological agent-based models-or ABMs-of the SARS-CoV-2 pandemic methodologically. The rapid spread of the outbreak requires fast-paced decision-making regarding mitigation measures. However, the evidence for the efficacy of non-pharmaceutical interventions such as imposed social distancing and school or workplace closures is scarce: few observational studies use quasi-experimental research designs, and conducting randomized controlled trials seems infeasible. Additionally, evidence from the previous coronavirus outbreaks of SARS and MERS lacks external validity, given the significant differences in contagiousness of those pathogens relative to SARS-CoV-2. To address the pressing policy questions that have emerged as a result of COVID-19, epidemiologists have produced numerous models that range from simple compartmental models to highly advanced agent-based models. These models have been criticized for involving simplifications and lacking empirical support for their assumptions. METHODS: To address these voices and methodologically appraise epidemiological ABMs, we consider AceMod (the model of the COVID-19 epidemic in Australia) as a case study of the modelling practice. RESULTS: Our example shows that, although epidemiological ABMs involve simplifications of various sorts, the key characteristics of social interactions and the spread of SARS-CoV-2 are represented sufficiently accurately. This is the case because these modellers treat empirical results as inputs for constructing modelling assumptions and rules that the agents follow; and they use calibration to assert the adequacy to benchmark variables. CONCLUSIONS: Given this, we claim that the best epidemiological ABMs are models of actual mechanisms and deliver both mechanistic and difference-making evidence. Consequently, they may also adequately describe the effects of possible interventions. Finally, we discuss the limitations of ABMs and put forward policy recommendations.