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-5251 Conference paper 2022 Salazar W.C.; Machado D.O.; Len A.J.G.; Gonzalez J.M.E.; Alba C.B.; De Andrade G.A.; Normey-Rico J.E. Neuro-Fuzzy Digital Twin of a High Temperature Generator IFAC-PapersOnLine 10.1016/j.ifacol.2022.07.081 Solar absorption plants are renewable energy systems with a special advantage: the cooling demand follows the solar energy source. The problem is that this plant presents solar intermittency, phenomenological complexity, and nonlinearities. That results in a challenge for control and energy management. In this context, this paper develops a Digital Twin of an absorption chiller High Temperature Generator (HTG) seeking accuracy and low computational efort for control and management purposes. A neuro-fuzzy technique is applied to describe HTG, internal Lithium-Bromide temperature, and water outlet temperature. Two Adaptative Neuro-Fuzzy Inference Systems (ANFIS) are trained considering real data of eight days of operation. Then, the obtained model is validated considering two days of real data. The validation shows a RMSE of 1.65e-2for the internal normalized temperature, and 2.05e-2for the outlet normalized temperature. Therefore, the obtained Digital Twin presents a good performance capturing the dynamics of the HTG with adaptive capabilities considering that each day can update the learning step. Β© 2022 Elsevier B.V.. All rights reserved.
publications-5252 Conference paper 2022 Vosinakis G.; Maltezos E.; Krommyda M.; Ouzounoglou E.; Amditis A. DATA INTEGRATION, HARMONIZATION AND PROVISION TOOLKIT FOR WATER RESOURCE MANAGEMENT AND PREDICTION SUPPORT WIT Transactions on the Built Environment 10.2495/FRIAR220071 Timely and reliable information is critical to organizations managing water resources. Drinking water is one main source of risk when its safety and security is not ensured. Early prediction and mitigation of such risks relies on prediction models that depend on live and historical data. Such data are quite heterogenous in nature, including sensor measurements, satellite imagery and radar readings, unmanned aerial vehicle (UAV) images and videos as well as results of prediction algorithms (flood risk, oil spills etc). AQUA3S is an EU funded project which combines novel technologies in water safety and security, aiming to standardize existing sensor technologies complemented by state-of-the-art detection mechanisms. Sensor networks are deployed in water supply networks and sources, supported by complex sensors for enhanced detection. Sensor measurements are supported by videos from UAVs, satellite images and social media observations from the citizens that report low-quality water in their area also creating social awareness and an interactive knowledge transfer. Semantic representation and data fusion provides intelligent decision support system (DSS) alerts and messages to the public through first responders’ mediums. This study presents the data ingestion, integration and harmonization platform that was developed to support the systems of the project, consisting of the necessary APIs, to ingest data, a harmonization layer and a data store layer The data is harmonized and indexed using the NGSI-LD model to make sure information can be indexed and served both is real time through a live context broker, as well as in the form of historical time series through a dedicated historical data service. The data store layer includes provisions for the storage of annotated binary files (images, videos, etc.) as well as georeferenced map layers following OGC protocols such as web feature service (WFS), web map service (WMS), and web coverage service (WCS). © 2022 WIT Press.
publications-5253 Conference paper 2022 CharvΓ΅t K.; Kozhukh D.; Kepka M.; HΓ΅jek P.; Ε nevajs H.; Kollerova M.; KubΓ­Δ_x008d_kovΓ΅ H.; LΓ¶ytty T.; Ssembajwe R.; Obot A.; Kantiza A.; Stephene S.; Ravid G.; Gelb E. Optimization of African LULC Database for Sustainable Development 2022 IST-Africa Conference, IST-Africa 2022 10.23919/IST-Africa56635.2022.9845582 Land use and land cover information in combination with other thematic datasets related to detailed reference spatial data in localities from an important dataset for different types of analyses in different domains. At the time being, when it comes to the strategy of the SDG, Green Deal, Destination Earth, and construction of Earth's digital twins, there is no model and database that would effectively gather information about the Earth's surface in sufficient detail and complex relations. The situation is much worse in Africa compared to Europe since there exist only scattered map layers from public sources across all Africa like Africover and CCI Land Cover 2016. Moreover, the other close option 'OpenStreetMap' with a continent-wide coverage collects data on a voluntary basis with minimal attributes. For this reason, it's prudent to provide validation and harmonisation of this data. Therefore, there was a focus on developing and optimising a new solution based on the Open Land Use (OLU) 2.0 data model. The OLU 2.0 database combined various thematic data with the most detailed reference geometry available. Thematic datasets were focused primarily on the information of land use and land cover and additionally on other themes like soil, topographic characteristics, climatic parameters, data from classification of remote sensing data, vegetation indices of field blocks, etc. and in different time periods. In this way, OLU4Africa 2.0 defined a model which can have large potential in Africa for high-end applications such as food security modelling; environment, biodiversity, and ecosystem protection; planning purposes; forest and water protection. It is worth mentioning that the OLU4Africa 2.0 application has been nominated among WSIS Prizes 2022. Β© 2022 IST-Africa Institute and Authors.
publications-5254 Conference paper 2022 Poore S.B.; Alden R.E.; Gong H.; Ionel D.M. Multi-Physics and Artificial Intelligence Models for Digital Twin Implementations of Residential Electric Loads 11th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2022 10.1109/ICRERA55966.2022.9922831 Heating, ventilation, and air-conditioning (HVAC) and electric water heating (EWH) represent residential loads. Simulating these appliances for electric load forecasting, demand response (DR) studies, and human behavior analysis using physics-based models and artificial intelligence (AI) can further advance smart home technology. This paper explains the background of residential load modeling, starting with the concept of digital twin (DT) as well as the different types of methods. Two major types of appliance load monitoring (ALM) and their advantages/disadvantages are then discussed. This is followed by a review of multiple studies on residential load modeling, particularly for HVAC, EWH, and human behavior. Further examples of electric load forecasts and DR case studies using experimental smart homes are provided. The results and impact of these studies are discussed, as well as their contribution to the advancement of smart home technology and large-scale application. Β© 2022 IEEE.
publications-5255 Book chapter 2022 Weingartner A.; Raich J. Smart Sensors for Smart Waters Springer Water 10.1007/978-3-031-08262-7_13 This chapter is looking back on 20Β years of online water quality monitoring, focussing on important achievements during that period, describe the current state of research and technology, and will take the oracle’s perspective at current and future trends. An overview of water monitoring topics is given, by method, measured substance, instrument, and by their applications. Focus is on substances of special environmental or health concern that can be detected by β€_x009c_solid stateβ€_x009d_ sensors, and on applications that are a bit off the beaten monitoring track, to get a feeling for the always widening domain of on-line and real-time water quality monitoring. Β© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
publications-5256 Article 2022 Bianco N.; Mauro A.W.; Mauro G.M.; Pantaleo A.M.; Viscito L. A semi-empirical model for de-watering and cooling of leafy vegetables Applied Thermal Engineering 10.1016/j.applthermaleng.2022.118227 This paper presents a semi-empirical model for mass and heat transfer applied to de-watering and cooling of fresh leafy vegetables. This process aims at optimizing vegetables’ moisture content and temperature through the interaction with conditioned airflows to ensure proper storage and preservation. It is implemented in different modules – i.e., a first set of hot modules with hot air, a second set of cold modules with cold air – allowing to remove water from the vegetables and to achieve the desired temperature. A dedicated transfer model is developed to follow the evolution of the liquid droplets on the leaves during the process. It is based on water mass and energy balances on product and air sides, where the bed of leaves is treated as a porous medium. The mass and heat transfer coefficients are calibrated by comparison with experimental data. The model is validated with real data from the field, and a parametric analysis is implemented to show its potential application to optimize the process. The calibrated model presents satisfactory reliability – less than ±1.0 °C as average error for output temperature – according to the uncertainty of the approaches available in literature, thereby ensuring a robust performance assessment. This can support the process application in several fields of the agri-food industry with significant quality and productivity improvements. Finally, the model can be used to develop digital twins to foster the ongoing digitalization of the agri-food sector with a view to sustainability. © 2022 Elsevier Ltd
publications-5257 Conference paper 2022 Shi Z.; Chen G.; Guan G.; Dong H. Research on Fatigue Life Intelligent Control System of Deepwater Jacket Platform based on Digital Twin 2022 IEEE 4th International Conference on Power, Intelligent Computing and Systems, ICPICS 2022 10.1109/ICPICS55264.2022.9873582 With the improvement of the design and manufacturing level of jacket platforms, there is a breakthrough in the design, manufacturing, and installation of the platform in China. There are many rods of deep-water jacket platforms. If the manual operation and maintenance mode is continued, the maintenance cost is very expensive, and the safety and reliability of the platform are difficult to guarantee. Through the two-way data transmission between the physical layer and the model layer of the digital twin system, the virtual and real image comparison is realized to obtain the stress data results of dangerous nodes, realize the real-time stress state data analysis, early warning function, and fatigue life prediction of the platform, and use the three-dimensional data simulation visual effect expression to feedback the detection results to the on-site workers to ensure the safety of underwater facilities. Β© 2022 IEEE.
publications-5258 Conference paper 2022 Li L.; Chen T.; Kong Q. The Study on Problems and Solutions of Digital Twin Technology Application under River Chief System 2022 8th International Conference on Hydraulic and Civil Engineering: Deep Space Intelligent Development and Utilization Forum, ICHCE 2022 10.1109/ICHCE57331.2022.10042548 As technology continues to develop, digital twin technology is gradually being used in water information technology. However, the current digital twin system still suffers from a number of difficulties, such as a lack of data, system coordination, and input and output. Based on this, this paper explores the problems and challenges of combining the digital twin technology with the river chief system and proposes policy recommendations to improve the data management system, address the relationship between people and technology, create an open digital twin public service platform, increase investment in research, and strengthen the training of complex talents. Β© 2022 IEEE.
publications-5259 Article 2022 Priyanka E.B.; Thangavel S.; Gao X.-Z.; Sivakumar N.S. Digital twin for oil pipeline risk estimation using prognostic and machine learning techniques Journal of Industrial Information Integration 10.1016/j.jii.2021.100272 Digital Twin technology is emerging as the digitization platform to enhance the industrial information processing and management in concern with virtual and physical entities. It paves the path for integrated industrial data analysis by combining IoT and Artificial Intelligence for better data interpretation. At present in oil industry, pipelines prevail to be feasible mode, the risk probability rate is getting increased and maintenance system becomes difficult with attention to the earlier prediction of accidents risks by undertaking entire pipeline. This paper aims to provide the frame structure of Digital Twin based on machine learning and prognostics algorithms model to analyze and predict the risk probability rate of oil pipeline system. Prognostics focuses on the detection of a failure precursor by estimating risk condition with respect to the pressure data towards the evaluation of remaining useful life (RUL). The abnormality of pressure attribute is taken in prognostic analysis for risk probability estimation followed by Dirichlet Process Clustering and Canopy clustering to segregate the abnormal pressure drop and rise. Using multiple oil substation data integration platform, the features are extracted using manifold learning methods and the best feature probability rates are evaluated using kernel based SVM algorithm to provide on-time control action on the entire oil pipeline system through efficient wireless data communication between server and the oil substations. As a result, the proposed work creates Virtual Intelligent Integrated Automated Control System to predict the risk rate in oil industry by integrating entire transmission lines through enhanced wireless information networks in remote locations. Β© 2021
publications-5260 Book chapter 2022 Kijak R. Water 4.0: Enhancing Climate Resilience The Palgrave Handbook of Climate Resilient Societies: Volumes 1-2 10.1007/978-3-030-42462-6_123 For this chapter, water 4.0 is defined as the industry 4.0 concept applied to the water sector. As industry 4.0 reflects the fourth industrial revolution, water 4.0 reflects the fourth water revolution. Based on the literature review and case studies, this chapter examines a proposition that water 4.0 will increase not only the sector’s economic effectiveness but also sustainability including climate resilience. Relevant technologies include digital twins, visualization, wireless monitoring sensors, industrial internet of things (IoT/IIoT), cloud computing, and predictive or prescriptive analytics but also blockchain, drones, and cybersecurity. For water 4.0 becoming a reality, water utility companies need not only collect more data but also to have proper analytical tools in place to convert data into information supporting optimal decisions. The current tools should preferably be replaced by machine learning algorithms that are nonlinear, nonstationary, and dynamic and thus aligned closely with the real world. It has been suggested in this chapter that such disruptive technologies be introduced through an ISO 55001-based asset management system (AMS). ISO 19650 series supplements ISO 55001 and contains additional requirements for the AMS development by focusing particularly on asset information. For this purpose, the series provides assistance with big data and digital twins. Two approaches are applicable to the implementation of water 4.0 through AMS: adaptability and more traditional continuous improvement with the former considered in this chapter as preferred but requires a sufficient level of asset management maturity. Therefore, it might be prudent that every organization sets their own water 4.0-related standards and objectives in their own AMS and considers the preferred level of adaptability. Adaptability is arguably required for water 4.0 with adaptation bringing the greatest value. © Springer Nature Switzerland AG 2021.