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-4471	article	2022	Lin, Wei Chao and Lin, Wei-Chiang and Tsai, Chih Fong and Tsai, Chihβ€Fong and Zhong, Jia Rong and Zhong, Jia	Deep learning for missing value imputation of continuous data and the effect of data discretization	Knowledge Based Systems	10.1016/j.knosys.2021.108079
publications-4472	article	2022	Boindala, Sriman Pankaj and Boindala, Sriman Pankaj and Jaykrishnan, G. and Jaykrishnan, G. and Ostfeld, Avi and Ostfeld, Avi	Optimizing Water Quality Treatment Levels for Water Distribution Systems under Mixing Uncertainty at Junctions	Journal of Water Resources Planning and Management	10.1061/(asce)wr.1943-5452.0001544	A real-life water distribution system (WDS) contains uncertainty in numerous stages. This makes the optimal management and design of a WDS a complex problem. Water quality has also become a significant factor in the design and management of a WDS. Our objective was to incorporate water quality uncertainty in the WDS design problem. The mixing level was assumed to be uncertain and used to design the WDS such that the design was immune to the level of mixing. This method aimed to yield designs that satisfied the nodal concentration constraints irrespective of the mixing level in the junctions. Two optimization methodologies, robust optimization and info-gap decision theory combined with a cuckoo search optimization algorithm, were proposed to solve this problem. An illustrative example 4Γ—4 grid network was used to understand nonuniform mixing and explain the design methodology using both methodologies. Then these methodologies were applied to solve a similar treatment plant problem on a modified Fossolo network. The results also exhibited a significant variation in cost between complete mixing and nonuniform mixing. The WDS designs obtained from both methods were evaluated through Monte Carlo simulations.
publications-4473	article	2022	Ramos, Helena M. and Ramos, Helena M. and Morani, Maria Cristina and Morani, Maria Cristina and Carravetta, Armando and Carravetta, Armando and Fecarotta, Oreste and Fecarotta, Oreste and Adeyeye, Kemi and Adeyeye, Kemi and LΓ³pez-JimΓ©nez, P. Amparo and JimΓ©nez, Petra Amparo LΓ³pez and LΓ³pez-JimΓ©nez, Petra Amparo and SΓ΅nchez, Modesto PΓ©rez and SΓ΅nchez, Modesto PΓ©rez and PΓ©rez-SΓ΅nchez, Modesto	New Challenges towards Smart Systemsβ€™ Efficiency by Digital Twin in Water Distribution Networks	Water	10.3390/w14081304	Nowadays, in the management of water distribution networks (WDNs), particular attention is paid to digital transition and the improvement of the energy efficiency of these systems. New technologies have been developed in the recent years and their implementation can be crucial to achieve a sustainable level of water networks, namely, in water and energy losses. In particular, Digital Twins (DT) represents a very innovative technology, which relies on the integration of virtual network models, optimization algorithms, real time data collection, and smart actuators information with Geographic Information System (GIS) data. This research defines a new methodology for an efficient application of DT expertise within water distribution networks. Assuming a DMA of a real water distribution network as a case study, it was demonstrated that a fast detection of leakage along with an optimal setting of pressure control valves by means of DT together with an optimization procedure can ensure up to 28\% of water savings, contributing to significantly increase the efficiency of the whole system.
publications-4474	article	2022	Hollenbeck, Derek and Hollenbeck, Derek and Chen, YangQuan and Chen, YangQuan	A Digital Twin Framework for Environmental Sensing with sUAS	Journal of Intelligent and Robotic Systems	10.1007/s10846-021-01542-8	Abstract This paper proposes a digital twin (DT) framework for point source applications in environmental sensing (ES). The DT concept has become quite popular among process and manufacturing industries for improving performance and estimating remaining useful life (RUL). However, environmental behavior, such as in gas dispersion, is ever changing and hard to model in real-time. The DT framework is applied to the point source environmental monitoring problem, through the use of hybrid modeling and optimization techniques. A controlled release case study is overviewed to illustrate our proposed DT framework and several spatial interpolation techniques are explored for source estimation. Future research efforts and directions are discussed.
publications-4475	article	2022	Laubenbacher, Reinhard and Laubenbacher, Reinhard and Niarakis, Anna and Niarakis, Anna and Helikar, TomΓ΅Ε΅ and Helikar, T. and An, Gary and An, Gary and Shapiro, Bruce E. and Shapiro, B. and Sheriff, Rahuman S. Malik and Malik-Sheriff, Rahuman S and Sego, T. J. and Sego, T. J. and Knapp, ΓdΓ΅m and Knapp, Adam and Macklin, Paul and Macklin, P. and Glazier, James A. and Glazier, James A.	Building digital twins of the human immune system: toward a roadmap	npj digital medicine	10.1038/s41746-022-00610-z	Abstract Digital twins, customized simulation models pioneered in industry, are beginning to be deployed in medicine and healthcare, with some major successes, for instance in cardiovascular diagnostics and in insulin pump control. Personalized computational models are also assisting in applications ranging from drug development to treatment optimization. More advanced medical digital twins will be essential to making precision medicine a reality. Because the immune system plays an important role in such a wide range of diseases and health conditions, from fighting pathogens to autoimmune disorders, digital twins of the immune system will have an especially high impact. However, their development presents major challenges, stemming from the inherent complexity of the immune system and the difficulty of measuring many aspects of a patientβ€™s immune state in vivo. This perspective outlines a roadmap for meeting these challenges and building a prototype of an immune digital twin. It is structured as a four-stage process that proceeds from a specification of a concrete use case to model constructions, personalization, and continued improvement.
publications-4476	article	2022	Wei, Yuying and Wei, Yuying and Li, Tian and Zhou, Kun and Law, Adrian Wingβ€Keung and Yang, Chun and Yang, Chun and Tang, Di and Tang, Di	Combined Anomaly Detection Framework for Digital Twins of Water Treatment Facilities	Water	10.3390/w14071001	Digital twins of cyber-physical systems with automated process control systems using programmable logic controllers (PLCs) are increasingly popular nowadays. At the same time, cyber-physical security is also a growing concern with system connectivity. This study develops a combined anomaly detection framework (CADF) against various types of security attacks on the digital twin of process control in water treatment facilities. CADF utilizes the PLC-based whitelist system to detect anomalies that target the actuators and the deep learning approach of natural gradient boosting (NGBoost) and probabilistic assessment to detect anomalies that target the sensors. The effectiveness of CADF is verified using a physical facility for water treatment with membrane processes called the Secure Water Treatment (SWaT) system in the Singapore University of Technology and Design. Various attack scenarios are tested in SWaT by falsifying the reported values of sensors and actuators in the digital twin process. These scenarios include both trivial attacks, which are commonly studied, as well as non-trivial (i.e., sophisticated) attacks, which are rarely reported. The results show that CADF performs very well with good detection accuracy in all scenarios, and particularly, it is able to detect all sophisticated attacks while ongoing before they can induce damage to the water treatment facility. CADF can be further extended to other cyber-physical systems in the future.
publications-4477	article	2024	Grigg, Neil S.	Water Distribution Systems: Implementation Status of Innovative Management Methods and Tools		10.1061/jpsea2.pseng-1535	Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.
publications-4478	article	2011	Zechman, Emily M. and Zechman, Emily M. and Zechman, Emily M. and Zechman, Emily M.	Agent-based modeling to simulate contamination events and evaluate threat management strategies in water distribution systems.	Risk Analysis	10.1111/j.1539-6924.2010.01564.x	In the event of contamination of a water distribution system, decisions must be made to mitigate the impact of the contamination to protect public health. Making threat management decisions while the contaminant is spreading through the network is a difficult process due to uncertainty and lack of monitoring data. This is further complicated by the response actions taken by the utility managers and water consumption choices made by the consumers as they all will affect the hydraulics, thus the spread of the contaminant plume, in the network. A modeling framework that allows the simulation of a contamination event under the effects of actions taken by utility managers and consumers will be a useful tool for the analysis of alternative threat mitigation and management strategies. The complex interactions between the managersβ€™ network operation decisions and consumersβ€™ water consumption choices, and the response of the hydraulics and contaminant transport in the water distribution system will be simulated using an agent-based modeling approach. Agent-based models are simulated individuals that are formulated as interacting autonomous entities. Each agent selects actions based on a set of rules that represent an individualβ€™s autonomy, goal-based desires, and reaction to the environment and the actions of other agents. This paper presents a multi-agent modeling framework that will combine agent-based, mechanistic, and dynamic methods. As actions taken by agents affect demands and flows in the system, dynamic approaches will update the mechanistic model and the identification of the contaminant source to supply the β€_x009c_utility managerβ€_x009d_ agents with the latest information as it becomes available. The framework will be designed to consider the typical issues involved in water distribution threat management and will provide valuable analysis of threat containment strategies for water distribution system contamination events.
publications-4479	article	2018	Shafiee, M. Ehsan and Berglund, Emily Zechman and Lindell, Michael K.	An Agent-based Modeling Framework for Assessing the Public Health Protection of Water Advisories	Water Resources Management	10.1007/s11269-018-1916-6	In the event that pathogens or toxins are introduced to a water distribution system, a utility manager may identify a threat through water quality data or alerts from public health officials. The utility manager may issue water advisories to warn consumers to reduce water use activities. As consumers react and change water demands, dynamic feedbacks among the community, utility managers, and the engineering infrastructure can create unexpected public health consequences and network hydraulics. A Complex Adaptive System (CAS)-based methodology is developed to couple an engineering model of a water distribution system with agent-based models (ABM) of consumers, public health officials, and utility managers to simulate feedback among management decisions, system hydraulics, and public behavior. A utility manager and a public health official are represented as agents, who respond to the event using a set of rules and equations that are based on a statistical analysis of a set of recorded water events. Consumers are represented as agents who update their water activities based on exposure to the contaminant and warnings from a utility agent and family members. A model of consumer compliance is developed using results from two surveys that report data to characterize consumer perceptions toward information sources during a water contamination event. The ABM framework is applied for an illustrative mid-sized virtual city to quantify the significance of interactions and advisories on public health consequences.
publications-4480	article	2020	Abdulkareem, Shaheen A. and Abdulkareem, Shaheen A. and Augustijn, Ellen-Wien and Augustijn, Ellen-Wien and Filatova, Tatiana and Filatova, Tatiana and MusiaΕ‚, Katarzyna and Musial, Katarzyna and Mustafa, Yaseen T. and Mustafa, Yaseen T.	Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning.	PLOS ONE	10.1371/journal.pone.0226483	Modern societies are exposed to a myriad of risks ranging from disease to natural hazards and technological disruptions. Exploring how the awareness of risk spreads and how it triggers a diffusion of coping strategies is prominent in the research agenda of various domains. It requires a deep understanding of how individuals perceive risks and communicate about the effectiveness of protective measures, highlighting learning and social interaction as the core mechanisms driving such processes. Methodological approaches that range from purely physics-based diffusion models to data-driven environmental methods rely on agent-based modeling to accommodate context-dependent learning and social interactions in a diffusion process. Mixing agent-based modeling with data-driven machine learning has become popularity. However, little attention has been paid to the role of intelligent learning in risk appraisal and protective decisions, whether used in an individual or a collective process. The differences between collective learning and individual learning have not been sufficiently explored in diffusion modeling in general and in agent-based models of socio-environmental systems in particular. To address this research gap, we explored the implications of intelligent learning on the gradient from individual to collective learning, using an agent-based model enhanced by machine learning. Our simulation experiments showed that individual intelligent judgement about risks and the selection of coping strategies by groups with majority votes were outperformed by leader-based groups and even individuals deciding alone. Social interactions appeared essential for both individual learning and group learning. The choice of how to represent social learning in an agent-based model could be driven by existing cultural and social norms prevalent in a modeled society.