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–² Type Year Authors Title Venue/Journal DOI Research type Water System Technical Focus Abstract Link with Projects Link with Tools Related policies ID
publications-5151 Conference paper 2023 Mori J.; Saito Y.; Kaji H. Study on Reduced-Order Modeling Technique of Three-Phase Voltage Imbalance Model 2023 62nd Annual Conference of the Society of Instrument and Control Engineers, SICE 2023 10.23919/SICE59929.2023.10354199 When using digital twins to create a simulation in the cyber space, their plant models must sequentially predict physical phenomena such as energy and water. However, increase in calculation costs renders their execution in real time challenging in case of a large scale, and they must be executed with high accuracy. Employing the mainstream technique of reducing the computational cost involves data-driven modeling, which suffers limited extrapolation accuracy as it does not have a physical structure. This study derived the simultaneous physical equations of the microgrid electric power network for three-phase voltage imbalance of AC electric power. Further, simulations were performed based on the equations. Using this power network model, we demonstrated that reduced-order modeling techniques with physical structure can offer better accuracy than models without a physical structure. Β© 2023 Society of Instrument and Control Engineers - SICE.
publications-5152 Article 2022 Raza M.; Prokopova H.; Huseynzade S.; Azimi S.; Lafond S. Towards Integrated Digital-Twins: An Application Framework for Autonomous Maritime Surface Vessel Development Journal of Marine Science and Engineering 10.3390/jmse10101469 The use of digital twins for the development of Autonomous Maritime Surface Vessels (AMSVs) has enormous potential to resolve the increasing need for water-based navigation and safety at the sea. Aiming at the problem of lack of broad and integrated digital twin implementations with live data along with the absence of a digital twin-driven framework for AMSV design and development, an application framework for the development of a fully autonomous vessel using an integrated digital twin in a 3D simulation environment has been presented. Our framework has 4 layers which ensure that simulation and real-world vessel and the environment are as close as possible. Γ…boat, an in-house, experimental research platform for maritime automation and autonomous surface vessel applications, equipped with two trolling electric motors, cameras, LiDARs, IMU and GPS has been used as the case study to provide a proof of concept. Γ…boat, its sensors, and the environment have been replicated in a commercial, 3D simulation environment, AILiveSim. Using the proposed application framework, we develop obstacle detection and path planning systems based on machine learning which leverage live data from a 3D simulation environment to mirror the complex dynamics of the real world. Exploiting the proposed application framework, the rewards across training episodes of a Deep Reinforcement Learning model are evaluated for live simulated data in AILiveSim. Β© 2022 by the authors.
publications-5153 Conference paper 2023 Akimov L.; Badenko V.; Gaganova E.; Kashintseva V. The Environmentally-Efficient Canal District Design Respecting Urban Context Lecture Notes in Networks and Systems 10.1007/978-3-031-21219-2_326 The following paper discusses the development of the work-flow that can be used in practice for the urban environmentally-efficient attraction points design and restoration. We developed the work-flow on the basis of the example of the design of canal district restoration in the heart of the city of Bangkok. The reference concept was developed in the scope of the International Workshop of Ur-ban and Architectural Design X edition, organized by Politecnico di Milano and Chulalongkorn University. The design ideas developed in this project are present in the study. In this article we dis-cuss the importance of water districts restoration from environmental, social and economic points of view. The introduction of the work-flow of the canal district design that has its aim to meet the exist-ing urban context and all the important requirements is important for designers and urban planners, since it can help to resolve a number of questions that have to be properly studied. The importance of introduction of GIS and digital twin technologies to the landscape restoration projects is as well discussed. Β© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
publications-5154 Article 2022 Kravos A.; Kregar A.; Penga; Barbir F.; KatraΕ΅nik T. Real-time capable transient model of liquid water dynamics in proton exchange membrane Fuel Cells Journal of Power Sources 10.1016/j.jpowsour.2022.231598 Optimal control of liquid water dynamics plays an instrumental role in achieving optimised performance and prolonged lifetime of PEM Fuel Cells (PEMFC). Tackling these challenges calls for precise on-line monitoring and control tools such as coupled virtual observers taking into consideration also liquid water dynamics. The latter proves to be especially challenging to model due to varying retention and removal rates of liquid and gaseous water depending on the operating conditions thus representing a longstanding knowledge gap. To fill this gap, this contribution presents derivation of a 1D+1D system level physically motivated two-phase model of PEMFC, which enables consistent treatment of liquid water dynamics on the system level in all seven most influential regions of the PEMFC, namely membrane, anode and cathode channels, GDLs, and catalyst layers, while exhibiting real-time readiness with real-time factor of 0.0449. The model is extensively tested on single-cell data, which consists of five sets of experiments with different operating regimes and durations. Overall results exhibit good agreement with experimental data in all of the performed tests with R2 factors larger than 0.95. Newly developed features of the model enable its use in development of advanced control methodologies and hardware-in-the-loop as well as digital twin applications. Β© 2022 The Authors
publications-5155 Conference paper 2023 Laucelli D.; Spagnuolo S.; Rinaldi A.; Perrone G.; Berardi L.; Giustolisi O. A complete digital water experience to support real leakage management planning IOP Conference Series: Earth and Environmental Science 10.1088/1755-1315/1136/1/012001 In the next years digital transition of Water Distribution Networks (WDNs) will represent a long-standing challenge for triggering innovations in the WDNs management tasks. The ongoing impulse for innovation, boosted also by the economic resources for post-pandemic recovery, is opening novel opportunities for technicians and researchers in the context of Digital Water. This work reports the key steps where novel concepts and tools of Digital Water have been used to support consultant companies in designing WDN leakage management actions in a real WDN managed by Acquedotto Pugliese, in Southern Italy. The Digital Water Services (DWSs) were used to support key phases of the workflow, including validation of topological data and integration of georeferenced connection to private properties; calibration of advanced hydraulic model exploiting mass-balance in pressure-driven analysis; the application of strategies for DMA design and pressure control; and the planning of pipe rehabilitation. WDNetXL platform was used to develop WDN Digital Twin and exploit the DWSs. Applying the Digital Water experience to multiple WDNs managed by the same provider also enabled planning the best courses of actions accounting for investments vs. expected leakage reduction at global scale. Β© 2023 Institute of Physics Publishing. All rights reserved.
publications-5156 Article 2022 Lawes R.; Mata G.; Richetti J.; Fletcher A.; Herrmann C. Using remote sensing, process-based crop models, and machine learning to evaluate crop rotations across 20 million hectares in Western Australia Agronomy for Sustainable Development 10.1007/s13593-022-00851-y Remote sensing has been widely employed to identify crop types and monitor crop yields on farms. Here, we combine successive seasons of these products to identify crop rotations in each field across 20 million hectares of the Western Australian Wheatbelt. We used the APSIM crop model to define the starting soil water, temperature stresses, biomass, and crop yield to characterize the prevailing agro-environment of that field. These remote sensing data and APSIM crop modeling outputs were then combined, with machine learning, to predict the effect of the complex interaction between agro-environment and crop rotation on wheat yield. Predictions from machine learning are employed to evaluate the benefits or otherwise of crop rotation across Western Australia for every field in the study region. In general, if fields subjected to a wheat-cereal rotation were instead subjected to a wheat-canola rotation, then 68% of these fields were predicted to experience a yield increase of between 0 and 1850 kg ha-1. However, only 28% of fields planted to canola were predicted to have a yield benefit of 200 kg ha-1 or more on the following wheat crops. On average, annual pastures generated a slight yield penalty of 47 kg ha-1 to the following wheat crop. The findings from this study, using crop models, remote sensing, and machine learning, indicate that the benefits of break crops and pastures to farmers is less than the 400 to 600 kg ha-1 benefit commonly reported from field experiments. These management insights could underpin the development of future decision aids or agricultural digital twins for crop management decisions such as crop rotation planning. The approach provides farmers with tangible insights about their production using outputs from crop-based remote sensing and crop modeling. Β© 2022, Crown.
publications-5157 Conference paper 2022 Belcore E.; Di Pietra V. LAYING THE FOUNDATION FOR AN ARTIFICIAL NEURAL NETWORK FOR PHOTOGRAMMETRIC RIVERINE BATHYMETRY International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 10.5194/isprs-archives-XLVIII-4-W1-2022-51-2022 This work aims to test the effectiveness of artificial intelligence for correcting water refraction in shallow inland water using very high-resolution images collected by Unmanned Aerial Systems (UAS) and processed through a total FOSS workflow. The tests focus on using synthetic information extracted from the visible component of the electromagnetic spectrum. An artificial neural network is created using data of three morphologically similar alpine rivers. The RGB information, the SfM depth and seven radiometric indices are calculated and stacked in an 11-bands raster (input dataset). The depths are calculated as the difference between the Up component of the bathymetry cross-sections and the water surface quotas and constitute the dependent variable of the regression. The dataset is then scaled. The observations of one of the analyzed case studies are used as the unseen dataset to test the generalization capability of the model. The remaining observations are divided into test (20%) and training (80%) datasets. The generated NN is a 3-layer MLP model with one hidden layer and the Rectified Linear Unit (ReLU) and sigmoid activation functions. The weights are initialized to small Gaussian random values, and kernel regularizers, L1 and L2, are added to reduce the overfitting. Weights are updated with the Adam search technique, and the mean squared error is the loss function. The importance and significance of 11 variables are assessed. The model has a 0.70 r-squared score on the test dataset and 0.77 on the training dataset. The MAE is 0.06 and the RMSE 0.08, similar results obtained from the unseen dataset. Although the good metrics, the model shows some difficulties generalizing swallow depths. Β© Copyright:
publications-5158 Article 2022 Li X.; Luo J.; Li Y.; Wang W.; Hong W.; Liu M.; Li X.; Lv Z. Application of effective water-energy management based on digital twins technology in sustainable cities construction Sustainable Cities and Society 10.1016/j.scs.2022.104241 This work aims to effectively resolve the problem of waterlogging in cities and manage water resources in sustainable cities. Digital Twins (DTs) technology was applied to the Urban Drainage System (UDS) and solves the modeling and scheduling problems of emergency drainage through the core model construction method of DTs. Firstly, the components of the UDS that are necessary in the process of building the model were listed according to the entity elements of the five-dimensional (5D) DTs model. Then, this work analyzed the essential data of the DTs of UDS and the Data Collection method according to the data elements of the 5D DTs model. Finally, the Multi-Level Dynamic Priority and Importance Scheduling (MDPIS) algorithm was proposed based on the Fixed Priority Scheduling (FPS) algorithm, which was verified by the simulation experiment. The experimental results indicated that the MDPIS algorithm showed significant performance in the rainfall scene with large fluctuations compared with the FPS algorithm. Specifically, the average improvement ratio was the highest, reaching 49.81%; the overall improvement rate was constant at about 48%. The operation results showed an apparent correlation between the catchment parameters and the overflow loss of the pumping station. The improved MDPIS algorithm can effectively utilize the water storage capacity and drainage capacity of the pumping station and reduce overflow losses during rainfall by dynamically adjusting the priority to solve the problem of urban inland inundation. The DTs-based UDS proposed here can effectively mitigate the overflow loss and improve the working efficiency of the pumping station cluster, promoting the development of Substainable Cities. Β© 2022
publications-5159 Article 2022 Nielsen R.E.; Papageorgiou D.; Nalpantidis L.; Jensen B.T.; Blanke M. Machine learning enhancement of manoeuvring prediction for ship Digital Twin using full-scale recordings Ocean Engineering 10.1016/j.oceaneng.2022.111579 Digital Twins have much attention in the shipping industry, attempting to support all phases of a vessel's life cycle. With several tools appearing in Digital Twin software suites, high-quality manoeuvring and performance prediction remain cornerstones. Propulsion efficiency is in focus while in service. Simulator-based training is in focus to ensure safety of manoeuvring in confined waters and harbours. Prediction of ships’ velocity and turn rate are essential for correct look and feel during training, but phenomena like dynamic inflow to propellers, bank and shallow water effects limit simulators’ accuracy, and master mariners often comment that simulations could be in better agreement with actual behaviours of their vessel. This paper focuses on digital twin enhancements to better match reality. Using data logged during in-service operation, we first consider a system identification perspective, employing a first-principles model structure. Showing that a complete first-principles model is not identifiable under the excitation met in service, we employ a Recurrent Neural Network to predict deviations between measured velocities and the model output. The outcome is a hybrid of a first-principles model with a machine learning generic approximator add-on. The paper demonstrates significant improvements in prediction accuracy of both in-harbour manoeuvring and shallow water passage conditions. © 2022 The Author(s)
publications-5160 Article 2023 Eklund M.; Sierla S.A.; Niemisto H.; Korvola T.; Savolainen J.; Karhela T.A. Using a Digital Twin as the Objective Function for Evolutionary Algorithm Applications in Large Scale Industrial Processes IEEE Access 10.1109/ACCESS.2023.3254896 In this paper, we describe how the up-to-date state of a digital twin, and its corresponding simulation model, can be used as a fitness function of an evolutionary algorithm for optimizing a large-scale industrial process. An ICT architecture is presented for solving the computational challenges that arise when the fitness function evaluation takes considerable amount of time. Parallel computation of the fitness function in a cloud computing environment is proposed and the evolutionary algorithm is connected to the computational environment using the Function-as-a-Service approach. A case-study was conducted on the district heating network of Espoo, the second largest city in Finland. The study shows that the architecture is suited for optimizing the operating costs of the large district heating network, with over 800 km of water pipes and over 14 heat producers, reaching a cost-saving of an average of 2%, and up-to 4%, over the current industrial state-of-the-art method in use at the city of Espoo. Β© 2013 IEEE.