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 â–˛ Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-5221 Article 2023 Payne K.; Chami P.; Odle I.; Yawson D.O.; Paul J.; Maharaj-Jagdip A.; Cashman A. Machine Learning for Surrogate Groundwater Modelling of a Small Carbonate Island Hydrology 10.3390/hydrology10010002 Barbados is heavily reliant on groundwater resources for its potable water supply, with over 80% of the island’s water sourced from aquifers. The ability to meet demand will become even more challenging due to the continuing climate crisis. The consequences of climate change within the Caribbean region include sea level rise, as well as hydrometeorological effects such as increased rainfall intensity, and declines in average annual rainfall. Scientifically sound approaches are becoming increasingly important to understand projected changes in supply and demand while concurrently minimizing deleterious impacts on the island’s aquifers. Therefore, the objective of this paper is to develop a physics-based groundwater model and surrogate models using machine learning (ML), which provide decision support to assist with groundwater resources management in Barbados. Results from the study show that a single continuum conceptualization is adequate for representing the island’s hydrogeology as demonstrated by a root mean squared error and mean absolute error of 2.7 m and 2.08 m between the model and observed steady-state hydraulic head. In addition, we show that data-driven surrogates using deep neural networks, elastic networks, and generative adversarial networks are capable of approximating the physics-based model with a high degree of accuracy as shown by R-squared values of 0.96, 0.95, and 0.95, respectively. The framework and tools developed are a critical step towards a digital twin that provides stakeholders with a quantitative tool for optimal management of groundwater under a changing climate in Barbados. These outputs will provide sound evidence-based solutions to aid long-term economic and social development on the island. © 2022 by the authors.
publications-5222 Article 2022 Pedersen A.N.; Pedersen J.W.; Borup M.; Brink-Kjar A.; Christiansen L.E.; Mikkelsen P.S. Using multi-event hydrologic and hydraulic signatures from water level sensors to diagnose locations of uncertainty in integrated urban drainage models used in living digital twins Water Science and Technology 10.2166/wst.2022.059 Digital twins of urban drainage systems require simulation models that can adequately replicate the physical system. All models have their limitations, and it is important to investigate when and where simulation results are acceptable and to communicate the level of performance transparently to end users. This paper first defines a classification of four possible 'locations of uncertainty' in integrated urban drainage models. It then develops a structured framework for identifying and diagnosing various types of errors. This framework compares model outputs with in-sewer water level observations based on hydrologic and hydraulic signatures. The approach is applied on a real case study in Odense, Denmark, with examples from three different system sites: a typical manhole, a small flushing chamber, and an internal overflow structure. This allows diagnosing different model errors ranging from issues in the underlying asset database and missing hydrologic processes to limitations in the model software implementation. Structured use of signatures is promising for continuous, iterative improvements of integrated urban drainage models. It also provides a transparent way to communicate the level of model adequacy to end users. Β© 2022 IWA Publishing. All rights reserved.
publications-5223 Article 2022 Danilov-Danilyan V.I. Digitalization of the 2020s and Cybernetization of the 1960s: Comparisons and Lessons Herald of the Russian Academy of Sciences 10.1134/S1019331622060028 Abstract: The causes of failures with the introduction of mathematical methods and computer technology inΒ the management of the Soviet economy in the 1960s are analyzed. The inadequacy of the models intended to build a system for the optimal functioning of the economy to the purposes for which this system was created is shown. According to the author, a similar situation may recur during mass digitalization. The problem of so-called digital twins is raised, primarily in relation to natural (primarily water), economic, and social objects. As in the 1960s, there has been a tendency to ignore the problem of the adequacy of a digital twin under construction to its object in accordance with the purpose of modeling. Β© 2022, Pleiades Publishing, Ltd.
publications-5224 Review 2022 Niknam A.; Zare H.K.; Hosseininasab H.; Mostafaeipour A.; Herrera M. A Critical Review of Short-Term Water Demand Forecasting Tools—What Method Should I Use? Sustainability (Switzerland) 10.3390/su14095412 The challenge for city authorities goes beyond managing growing cities, since as cities develop, their exposure to climate change effects also increases. In this scenario, urban water supply is under unprecedented pressure, and the sustainable management of the water demand, in terms of practices including economic, social, environmental, production, and other fields, is becoming a must for utility managers and policy makers. To help tackle these challenges, this paper presents a well-timed review of predictive methods for short-term water demand. For this purpose, over 100 articles were selected from the articles published in water demand forecasting from 2010 to 2021 and classified upon the methods they use. In principle, the results show that traditional time series methods and artificial neural networks are among the most widely used methods in the literature, used in 25% and 20% of the articles in this review. However, the ultimate goal of the current work goes further, providing a comprehensive guideline for engineers and practitioners on selecting a forecasting method to use among the plethora of available options. The overall document results in an innovative reference tool, ready to support demand-informed decision making for disruptive technologies such as those coming from the Internet of Things and cyber–physical systems, as well as from the use of digital twin models of water infrastructure. On top of this, this paper includes a thorough review of how sustainable management objectives have evolved in a new era of techno-logical developments, transforming data acquisition and treatment. © 2022 by the authors. Li-censee MDPI, Basel, Switzerland.
publications-5225 Conference paper 2022 Teixeira R.; Puccinelli J.; De Vargas Guterres B.; Pias M.R.; Oliveira V.M.; Botelho S.S.D.C.; Poersch L.; Filho N.D.; Janati A.; Paris M. Planetary Digital Twin: A Case Study in Aquaculture Proceedings of the ACM Symposium on Applied Computing 10.1145/3477314.3508384 Precision agriculture and aquaculture offer a new way of creating a production system that is efficient and eco-friendly with the potential to increase food availability to wider communities across the globe. Digital technologies and applied computing play a crucial role to aid local farmers in better control of production losses. This paper addresses whether it is feasible to deploy a virtual digital replica of aquaculture farms to control essential water quality variables, including temperature, dissolved oxygen (DO), pH, turbidity and ammonia. Traditional IoT systems can offer monitoring capabilities. However, this emerging application requires real-time control-based actuation to intervene in the environment. Often, farmers have limited time to react to any anomalous event. The proposed Planetary Digital Twin employs AI-based control loops to monitor, simulate and optimize aquaculture processes and assets. Preliminary system validation results suggest the approach is feasible in communicating real-time sensor data to a mechanism to detect and control anomalies. Β© 2022 Owner/Author.
publications-5226 Article 2022 Sauder T.; Mainçon P.; Lone E.; Leira B.J. Estimation of top tensions in mooring lines by sensor fusion Marine Structures 10.1016/j.marstruc.2022.103309 The paper describes a sensor fusion method that provides reliable, uninterrupted and bias-free estimates of the top tension in a mooring line. The method exploits the geometric nonlinearity of mooring systems installed in shallow to moderate water depths: a change of line length (due to winching) affects the local dynamic stiffness of the mooring line. Based on measurements of fairlead displacements and of the dynamic part of the top tension, the line length and true (unbiased) mean tension can be inferred. The method combines the use of (1) a classical kinematic observer to derive fairlead motions, (2) the compression of the recent history of fairlead motions to a few parameters, (3) a bank of neural networks, each network modelling the response corresponding to a given line length/static tension, and (4) a heuristic approach to selecting the most promising model among the candidates. One major advantage of the method is its sparsity, making it computationally efficient so it can be applied both offline, on large sets of recorded historical data, and online running on lightweight embedded hardware. The paper presents in detail each component listed above, and the method as a whole is verified on a realistic case. Given that enough excitation is present, the estimator was found to converge towards the true value of the tension, and to cope well with transient conditions such as winching operations, and with the presence of oceanic current. © 2022 The Author(s)
publications-5227 Review 2022 Azimov U.; Avezova N. Sustainable small-scale hydropower solutions in Central Asian countries for local and cross-border energy/water supply Renewable and Sustainable Energy Reviews 10.1016/j.rser.2022.112726 The Central Asian area is confronted with a number of acute obstacles as it attempts to transition to a long-term electrical power supply. Small-scale hydropower systems may be a viable answer to these problems. Central Asian nations' hydropower resources are allocated unevenly. Regardless, it remains the most exploitable renewable energy source in the area, with both Kyrgyzstan and Tajikistan possessing some of the world's highest hydropower potential. Nonetheless, for fossil-fuel-rich nations like Uzbekistan, Turkmenistan, and Kazakhstan, hydropower will play a significant role in the future energy balance. Furthermore, because rivers often run across many boundaries, water security plays an important role in cross-border relations between Central Asian countries. To achieve effective exploitation of small hydropower potential, technological and financial expenditures are needed to improve the levelised cost of energy (LCOE) of diverse hydroelectric equipment by increasing lifetime, improving efficiency, and increasing yearly power output. Several of these issues can be resolved by installing small and micro hydropower plants in the many minor rivers and irrigation canals. A pumped hydro energy storage system should also be tested and certified for better usability. A hydrological digital twin of relevant river system and irrigation network should be constructed to increase the understanding for performance and enable system-level improvement. Furthermore, optimal performance necessitates constant monitoring of the network, necessitating the development of intelligent monitoring employing sensors in conjunction with control systems and smart grid interactions. This review focuses on the broad and efficient use of these existing resources, which are still underutilized. Β© 2022 The Authors
publications-5228 Conference paper 2023 Tshabalala P.; Kuriakose R.B. Designing an Experimental Setup for Digital Twins in Modern Manufacturingβ€”A Case Study Using a Water Bottling Plant Lecture Notes in Networks and Systems 10.1007/978-981-19-5221-0_58 The manufacturing world has evolved throughout the years and has become more autonomous and complex. Industry 4.0 and digital twins merge the gap between the physical and digital world and provide instant communication between the virtual and the physical product, as digitalization is a great tool to enhance production and exploit new Industry 4.0 opportunities. A digital twin is a model that is continuously updated with live data from the physical system to monitor the current status and predict the future behavior of the physical system. This paper puts forth the experimental setup for digital twins in modern manufacturing with the case study of a water bottling plant. The aim of the paper is to examine if the design of a digital twin can assist in reducing the cycle time between workstations and pre-determine possible bottlenecks that might occur during production. This task is undertaken by first setting an introduction to the challenges faced by the manufacturing industry today and then proposing a design for the digital twin for a water bottling plant. The results of the digital shadow designed for the water bottling plant show that the lack of real-time inputs may have a significant impact on the cycle time. Β© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
publications-5229 Article 2022 Li F.; Vanrolleghem P.A. Including snowmelt in influent generation for cold climate WRRFs: comparison of data-driven and phenomenological approaches Environmental Science: Water Research and Technology 10.1039/d1ew00646k Influent generation models are developed to provide the influent disturbance at the inlet of a WRRF. A reliable influent model is important for WRRF design, upgrade and different digital twin studies. In this work, a data-driven methodology is proposed to create an influent generator (IG) model, which describes the influent flow and water temperature dynamics under the impact of snowmelt under cold climate conditions. The model structure applied was the long short-term memory (LSTM) artificial neural network with residual connection. The final result of influent generation for a Canadian case study is compared with a previously proposed phenomenological model. The performance is evaluated by different performance criteria and the results revealed that the LSTM approach has a better performance than the phenomenological model in terms of accuracy. In conclusion, the proposed model can successfully reproduce the influent dynamics of a combined sewer system's wastewater generation with snowmelt infiltration impacts. Β© 2022 The Royal Society of Chemistry.
publications-5230 Article 2022 Cerquera L.A.R.; Pinilla J.A.; Ratkovich N.R.; Asuaje M. Study of the Dynamic Behavior of an Autonomous Inflow-Control Device Using a Digital Twin Processes 10.3390/pr10122691 Solutions that aim to reduce water production in heavy oil wells have led to the design of devices known as rate-controlled production (RCP) autonomous inflow-control device (AICD) valves, which are placed in well completions and autonomously open with the oil inflow and close with water by choking the flow. These devices, which are based on Bernoulli’s principle, use a levitating disk that chokes the flow of the phase with the lowest flow resistance. This study proposes a numerical model based on computational fluid dynamics (CFD) technology to understand these devices’ operations and propose better designs without experimentation. The numerical model was based on dynamic fluid–body interaction (DFBI) and volume of fluid (VOF) models. The model was found to respond as expected depending on the physical properties of the fluids involved in heavy oil production. Finally, some limitations were found in the numerical study that can be improved in future studies. © 2022 by the authors.