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 Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-5071 Conference paper 2023 He W.; Chen Y.; Xu S.; Ding C.; Lee L.-H.; Kan G.L. Dragon Boat Simulation: An Immersive Experience Beyond Traditional Gaming Proceedings - SUI 2023: ACM Symposium on Spatial User Interaction 10.1145/3607822.3618022 Immersive sports are emerging recently after the metaverse hype. This paper investigates an immersive setup that leverages virtual reality to recreate the kinetic and sensory nuances of dragon boating. We employ a body-centric approach to enable users to engage with authentic paddling actions, e.g., mobilising iron sticks on the boat with reasonably emulated water resistance. The virtual environments mirror real-world scenes, e.g., Pearl River (Guangdong). We employed multi-modal experiences to replicate the visuals and tactile, kinesthetic, and environmental intricacies of dragon boating. Our Dragon Boat simulator serves as a groundwork for redefining the boundaries of immersive sports training that bridges the gap between virtuality and the real world. Β© 2023 Owner/Author.
publications-5072 Review 2023 Polimene L.; Parn O.; Garcia-Gorriz E.; Macias D.; Stips A.; Duteil O.; Ferreira-Cordeiro N.; Miladinova S.; Piroddi C.; Serpetti N. Should we reconsider how to assess eutrophication? Journal of Plankton Research 10.1093/plankt/fbad022 Eutrophication in marine waters is traditionally assessed by checking if nutrients, algal biomass and oxygen are below/above a given threshold. However, increased biomass, nutrient concentrations and oxygen demand do not lead to undesirable environmental effects if the flow of carbon/energy from primary producers toward high trophic levels is consistently preserved. Consequently, traditional indicators might provide a misleading assessment of the eutrophication risk. To avoid this, we propose to evaluate eutrophication by using a new index based on plankton trophic fluxes instead of biogeochemical concentrations. A preliminary, model-based, assessment suggests that this approach might give a substantially different picture of the eutrophication status of our seas, with potential consequences on marine ecosystem management. Given the difficulties to measure trophic fluxes in the field, the use of numerical simulations is recommended although the uncertainty associated with biogeochemical models inevitably affects the reliability of the index. However, given the effort currently in place to develop refined numerical tools describing the marine environment (Ocean Digital Twins), a reliable, model-based, eutrophication index could be operational in the near future. Β© 2023 The Author(s). Published by Oxford University Press.
publications-5073 Conference paper 2023 Pollert J.; Strogonov V. HARNESSING PHOTOGRAMMETRY FOR WHITEWATER SLALOM COURSES IN NATURAL RIVERBEDS: AN INNOVATIVE APPROACH FOR COURSE DESIGN AND ANALYSIS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 10.5194/isprs-archives-XLVIII-5-W2-2023-93-2023 Whitewater slalom, a thrilling and challenging water sport, requires carefully designed courses that provide a dynamic and engaging experience for athletes. Traditional methods of designing slalom courses in natural riverbeds have relied on manual measurements and estimations, which can be time-consuming, labour-intensive, and subject to human error. However, with the advent of photogrammetry, a cutting-edge technology that involves the use of aerial or ground-based cameras to capture and process 3D data from images, there is a new frontier in designing and analysing white water slalom courses. This article explores the usage of photogrammetry in the context of white-water slalom course design in natural riverbeds. It highlights how photogrammetry can revolutionize the course design process by providing accurate and detailed 3D models of the riverbed terrain, which can be used for virtual simulations, rapid prototyping, and precise analysis of different design options. The advantages of using photogrammetry include improved accuracy, efficiency, and cost-effectiveness in comparison to traditional methods. The article concludes by discussing the challenges and limitations of using photogrammetry for whitewater slalom course design, including issues related to data acquisition, processing, and accuracy. Despite these challenges, the potential of photogrammetry in revolutionizing the design and analysis of white-water slalom courses in natural riverbeds is immense, and it opens up exciting opportunities for further research, innovation, and advancement in the field of water sports. Β© 2023 J. Pollert.
publications-5074 Article 2023 Cesco S.; Sambo P.; Borin M.; Basso B.; Orzes G.; Mazzetto F. Smart agriculture and digital twins: Applications and challenges in a vision of sustainability European Journal of Agronomy 10.1016/j.eja.2023.126809 Smart agriculture – i.e., the increasing use of information technologies, sensors, autonomous vehicles, data analytics, predictive modelling, and other digital technologies related to agricultural activities – has been strongly argued for as a means to significantly contribute to increased food security, reduced water consumption, reduced fertilizer and pesticide input, and increased farm profitability. Despite this, the adoption rate of smart agricultural technologies is still low and varies significantly according to the specific technology and the geographical area considered. The goals of this paper are to: (1) propose a conceptual framework for smart agriculture and digital twins, which takes into account the needs and characteristics of the farms; (2) present the application of the proposed conceptual framework as a case study; and (3) shed light on the challenges of and the future perspectives on smart agriculture. We first propose a framework for the design of farm information systems consisting of four key phases (i.e., data collection, data processing, data analysis and evaluation, and information use) based on the infological approach. We then apply the framework to present and discuss a field application of smart agriculture and digital twins on crop nitrogen (N) fertilization. The case study, along with the cited literature, highlights the need to specify the optimal N fertilizer input as well as defining the spatial variability of the land area, the soil characteristics and crop yield, and the integration of these with temporal variability. Finally, we discuss challenges and future perspectives, with particular focus on geographical areas characterized by small average farm size. We argue that, thanks to digital twins, the wide set of data collected can enable predictive (and stability) analyses that if implemented can benefit the farmer and the environmental, social, and economic sustainability of the agricultural system. © 2023 Elsevier B.V.
publications-5075 Book chapter 2023 Yadav S.S. The convergence of digital twin, internet of things, and artificial intelligence: Digital smart farming Handbook of Research on Applications of AI, Digital Twin, and Internet of Things for Sustainable Development 10.4018/978-1-6684-6821-0.ch025 An agricultural digital twin is presented in this research using technologies from the sensing change and smart water management platform projects. Unlike the sensing change project, which created a soil probe, an internet of things is now being developed by the SWAMP project platform for managing water. The authors come to the conclusion that this system is capable of collecting data from the soil probe and displaying it in a dashboard, allowing for the deployment of additional soil probes, as well as other monitoring and controlling devices, to create a fully functional digital twin. Β© 2023, IGI Global. All rights reserved.
publications-5076 Article 2023 Choi Y.; Yoon S. In-situ observation virtual sensor in building systems toward virtual sensing-enabled digital twins Energy and Buildings 10.1016/j.enbuild.2022.112766 Sensing in building operations is essential to realize intelligent buildings and digital twin-enabled operations. However, buildings have a limited sensing environment with sensor absences, faulty sensors, low redundancy, and less dense deployment because of the inherent building characteristics (e.g., massive, heterogeneous, and long-term). It impedes the advanced building operations. To tackle these issues, this study proposes a novel sensing method for in-situ observation virtual sensors (OVS) in building operations. The proposed OVS is intended to predict the unmeasured variables in real time, without the target sensors. To do so, the OVS is modeled in-situ and then calibrated indirectly without the target observation (Y) so that the virtual sensing can be effectively applied in the building sector, where it is very difficult to establish a laboratory environment having Y for the OVS modeling. The OVS can be developed and operated in the building digital twins, thus extending the physical sensing coverages for digital twin-enabled intelligent operations. For real application, the proposed OVS was developed to observe the return water temperature of a real district heating system. The OVS was experimentally validated with the real measurement to discuss the in-situ OVS model and calibration performance. The OVS could be successfully modeled without using the target observation (Y), and indirect calibration improved the initial OVS performance by 32 %. The OVS demonstrates a significant performance with a root mean square error of 0.55 Β°C. Β© 2023 Elsevier B.V.
publications-5077 Article 2023 Cetina-QuiΓ±ones A.J.; SΓ΅nchez-DomΓ­nguez I.; Casillas-Reyes A.; Bassam A. 9E analysis of a flat plate solar collector system implementation: A new approach based on digital twin model coupled with global sensitivity analysis and multi-objective optimization Journal of Renewable and Sustainable Energy 10.1063/5.0151617 Flat plate solar collectors are technology with the most solar thermal energy field applications, and different studies based on artificial intelligence have been used to model these systems. This research study presents a 9E analysis based on a digital twin model coupled with global sensitivity analysis and multi-objective optimization of a solar system integrated with an array of flat plate solar collectors to satisfy residential hot water demand that represents a case study with different applications. A model based on artificial neural networks was trained, and a global sensitivity analysis using the Sobol method and a multi-objective optimization study using a genetic algorithm were also implemented. The main outcomes revealed that the digital twin model presented a high correlation above 0.99, and the 9E analysis reported a maximum value of 25.18% for thermal efficiency and 0.266% for exergetic efficiency. Also, a value of 1798.5 kgCO2/year was obtained for the amount of CO2 mitigated, $1342.9 USD for net present value, $0.0104 USD/kWh for levelized cost of energy, and 92.62, 0.519 kgCO2/year, $3.43, $1.34, and $0.00752 USD/year for energoenvironmental, exergoenvironmental, enviroeconomic energoenviroeconomic, and exergoenviroeconomic indicators, respectively. The methodology and the 9E analysis results provide a comprehensive approach that determines the optimal choice by analyzing the system's viability with different assessments and goes beyond the conventional analyses currently presented in the literature as it shows an untapped market potential for the best decision-making. Β© 2023 Author(s).
publications-5078 Conference paper 2023 Soufiane E.; Yousra E.K.; El Mahdi B.; Bachir E.K. Contribution to Maintenance 4.0 by Monitoring and Prognosis of Industrial Installations by Digital Twin: Case Study on Wastewater Filtration Pilot Proceedings of the IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 10.1109/IDAACS58523.2023.10348937 The digital solutions have taken an advance during the digital transition in the industrial development, the use of new technologies has allowed to better understand the behavior of the different parts of an industrial installation and to verify the interactions between the adjustment parameters and the optimal functioning of the production units and their impact on the competitiveness of the entity. A digital twin is a digital representation of a physical object (asset or process) that is updated in real time by the transfer of data between this physical object and its digital (virtual) model. What differentiates DT from simulation and other digital models or Computer Aided Design is the automatic bidirectional exchange of data between the digital and physical twins in real time. In this manuscript, we present a digital twin structure for monitoring a water filtration pilot. First, we propose a relational modeling of the digital twin layers of a production system with a detailed architecture of the digital twin based on the technologies used. Finally, a real-time supervision interface of the filtration process parameters is demonstrated as a first step in the implementation of a digital twin. Β© 2023 IEEE.
publications-5079 Article 2023 Tarpanelli A.; Bonaccorsi B.; Sinagra M.; Domeneghetti A.; Brocca L.; Barbetta S. Flooding in the Digital Twin Earth: The Case Study of the Enza River Levee Breach in December 2017 Water (Switzerland) 10.3390/w15091644 The accurate delineation of flood hazard maps is a key element of flood risk management policy. Flood inundation models are fundamental for reproducing the boundaries of flood-prone areas, but their calibration is limited to the information available on the areas affected by inundation during observed flood events (typically fragmentary photo, video or partial surveys). In recent years, Earth Observation data have supported flood monitoring and emergency response (e.g., the Copernicus Emergency Service) thanks to the proliferation of available satellite sensors, also at high spatial resolution. Under this umbrella, the study investigates a levee breach that occurred in December 2017 along the Enza River, a right tributary of the Po River, that caused the inundation of a large area including Lentigione village. The flood event is simulated with a 2D hydraulic model using satellite images to calibrate the roughness coefficients. The results show that the processing and the timing of the high-resolution satellite imagery is fundamental for a reliable representation of the flooded area. Β© 2023 by the authors.
publications-5080 Article 2023 Wang K.-X.; Xing T.-Y.; Zhu X.-L. Deep Learning-based Fault Diagnosis of Moisture Separator and Reheater Digital Twin System; [ε_x009f_ΊδΊ_x008e_ζ·±εΊ¦ε­¦δΉ η_x009a_„汽水分离ε†_x008d_ηƒ­ζ•°ε­—ε­η”_x009f_η³»η»_x009f_ζ•…ι_x009a__x009c_θ―_x008a_ζ–­η ”η©¶] Reneng Dongli Gongcheng/Journal of Engineering for Thermal Energy and Power 10.16146/j.cnki.rndlgc.2023.03.022 In order to solve the problem that the accuracy of traditional fault diagnosis model is limited by the scarcity of fault samples and the coupling of time dimension and variable dimension of fault data, a fault diagnosis method based on deep learning is proposed for complex industrial systems such as moisture separator and reheater system. Firstly, the digital twin system of moisture separator and reheater is constructed to establish the fault diagnosis data warehouse and solve the problem of scarcity of data samples. Secondly, based on the previous step, a fault diagnosis model based on deep residual network is constructed to diagnose the typical faults of steam water separation and reheat system, including uneven flow, break, deterioration of heat transfer and change of valve characteristics, so as to solve the problem of time-varying and multi-dimensional data variables. The simulation results show that the digital twin system can realize the accurate simulation of the steady-state, dynamic and fault conditions of the steam water separation and reheat system, and meet the data requirements of the subsequent in-depth learning model; the fault diagnosis model based on deep residual network can realize the fault diagnosis of time-varying and multi-dimensional industrial data. The T-distributed stochastic neighborhood embedding (TSNE) methodis used to visualize the model and verify that the suggested diagnostic model distinguishes significantly between different fault types of data. Β© 2023 Harbin Research Institute. All rights reserved.