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-4911 Conference paper 2024 Vaghefi E.; Hosseini S.; Mirkoohi E. A DEEP MLP-CNN MODEL USING METADATA TO PREDICT MELT POOL MORPHOLOGY IN LPBF: EFFECT OF GEOMETRICAL FACTORS Proceedings of ASME 2024 19th International Manufacturing Science and Engineering Conference, MSEC 2024 10.1115/MSEC2024-124641 The laser powder bed fusion (LPBF) process enables complex geometries to be created in bespoke parts. The LPBF process reduces lead times and costs for high-value parts by reducing material waste, unlike traditional subtractive manufacturing techniques. However, the fabrication of complex geometries using the LPBF process for industrial applications remains challenging in terms of achieving consistent mechanical properties. Experimental investigations show that geometrical factors such as shape and size impact the thermal history, microstructure, defect structure, and thus mechanical and fatigue properties. Consequently, the partially known effect of design parameters on part quality causes significant challenges in the standardization, qualification, and certification of additively manufactured (AM-ed) parts. In this paper, a deep Multilayer Perceptron and Convolutional Neural Network (MLP-CNN) is proposed to predict the melt pool depth for various geometries during LPBF of stainless steel 316 L using metadata (i.e., numerical and image data). Quite often the melt pool depth generated during the AM processes is employed as a surrogate for thermal, defect- and micro-structural signatures. To predict melt pool depth during the LPBF process of complex geometries, a digital twin environment using a finite element model (FEM) is developed and validated using experimental melt pool measurements for different geometries within multi-tracks and layers. The FEM was in agreement with experimentation with an error of less than 15%. Through the process simulation, a large dataset of melt pool depths was obtained for various part geometries at different locations and process parameters. Next, the MLP-CNN framework was established to identify the impact of part design and process parameters on melt pool depths. To enhance the performance and robustness of this model, data augmentation has been implemented by rotating and transferring geometries to artificially expand the dataset. Data augmentation helps to mitigate overfitting and promote better generalization, especially in the context of limited training data. After training, the proposed model is found to give accurate melt pool prediction even for new geometries not considered during training. The fusion of CNN and MLP has led to melt pool predictions for unseen geometries with an accuracy of 95%. Β© 2024 by ASME.
publications-4912 Review 2024 Shamshiri R.R.; Sturm B.; Weltzien C.; Fulton J.; Khosla R.; Schirrmann M.; Raut S.; Basavegowda D.H.; Yamin M.; Hameed I.A. Digitalization of agriculture for sustainable crop production: a use-case review Frontiers in Environmental Science 10.3389/fenvs.2024.1375193 The digitalization of agriculture is rapidly changing the way farmers do business. With the integration of advanced technology, farmers are now able to increase efficiency, productivity, and precision in their operations. Digitalization allows for real-time monitoring and management of crops, leading to improved yields and reduced waste. This paper presents a review of some of the use cases that digitalization has made an impact in the automation of open-field and closed-field cultivations by means of collecting data about soils, crop growth, and microclimate, or by contributing to more accurate decisions about water usage and fertilizer application. The objective was to address some of the most recent technological advances that are leading to increased efficiency and sustainability of crop production, reduction in the use of inputs and environmental impacts, and releasing manual workforces from repetitive field tasks. The short discussions included at the end of each case study attempt to highlight the limitations and technological challenges toward successful implementations, as well as to introduce alternative solutions and methods that are rapidly evolving to offer a vast array of benefits for farmers by influencing cost-saving measures. This review concludes that despite the many benefits of digitalization, there are still a number of challenges that need to be overcome, including high costs, reliability, and scalability. Most of the available setups that are currently used for this purpose have been custom designed for specific tasks and are still too expensive to be implemented on commercial scales, while others are still in their early stages of development, making them not reliable or scalable for widespread acceptance and adoption by farmers. By providing a comprehensive understanding of the current state of digitalization in agriculture and its impact on sustainable crop production and food security, this review provides insights for policy-makers, industry stakeholders, and researchers working in this field. Copyright Β© 2024 Shamshiri, Sturm, Weltzien, Fulton, Khosla, Schirrmann, Raut, Basavegowda, Yamin and Hameed.
publications-4913 Article 2024 Zhou W.; Jiao J.; Xu H.; Wei M.; Zhao X. PointBiMssc: Bidirectional Multiscale Attention-Based Point Cloud Semantic Segmentation for Water Conservancy Environment IEEE Geoscience and Remote Sensing Letters 10.1109/LGRS.2024.3432671 Point cloud semantic segmentation is a key technique for the digital twin construction of water conservancy projects, which can realize the identification and change detection of terrain features. However, constructing full-range point cloud data of the water conservancy environment remains one of the critical challenges in achieving comprehensive digital twin construction. Meanwhile, the openness of the water conservancy environment also makes its point cloud data structure highly complex, posing challenges to the accuracy and robustness of point cloud semantic segmentation algorithms. Therefore, we adopt unmanned aerial vehicle (UAV)-borne lidar to scan water conservancy scenes and construct a large-scale point cloud dataset, Water Conservancy Segment 3-D (WCS3D), with approximately 265 million points. On this basis, we propose a point cloud segmentation model named PointBiMssc based on a bidirectional multiscale attention mechanism for point cloud semantic segmentation in the water conservancy environment. A series of experiments conducted on the WCS3D dataset demonstrate that the PointBiMssc model can accurately complete point cloud semantic segmentation tasks and generate high-precision segmentation boundaries, outperforming the latest transformer-based models in mean intersection over union (mIoU) and overall pointwise accuracy (OA) evaluation metrics to achieve state-of-the-art performance. Code: https://github.com/JJBUP/PointBiMssc, dataset: https://github.com/XTU-SCS-HappyCV/WCS3D. Β© 2004-2012 IEEE.
publications-4914 Article 2024 Lee Y.; Jin C.; Kim M.; Xu W. Digital twin approach with minimal sensors for Riser's fatigue-damage estimation International Journal of Naval Architecture and Ocean Engineering 10.1016/j.ijnaoe.2024.100603 This study proposes riser fatigue monitoring based on digital twin models with a motion sensor attached to the platform and riser. The reference model was a spread-moored Floating Production Storage and Offloading (FPSO) with Steel Lazy-Wave Risers (SLWR). Coupled dynamics simulations under given environmental conditions were performed to generate synthetic sensor signals for digital twin models. Finite-element-based riser digital twin models were then constructed to run with the synthetic sensor inputs. A machine learning algorithm that estimates the 3D current profile along the water column was employed to improve the digital twin models by inputting the estimated current profile as additional loads. The digital twin models with or without the estimated current produce the time histories of behaviors and stresses along the riser, and the corresponding fatigue damage and life were estimated by the rainflow-counting method. The fatigue assessment results demonstrate its feasibility through small errors in fatigue damage. Β© 2024 The Society of Naval Architects of Korea
publications-4915 Conference paper 2024 Ramesh K.; Sasirekha G.V.K.; Rao M.; Bapat J.; Das D. Digital Twin based What-if Simulation of Security Attacks in Smart Irrigation Systems Proceedings of CONECCT 2024 - 10th IEEE International Conference on Electronics, Computing and Communication Technologies 10.1109/CONECCT62155.2024.10677126 The rapid integration of Internet of Things (IoT), machine learning and big data technologies in smart irrigation systems has significantly enhanced the agricultural efficiency and water resource management. However, it is vital to secure the smart irrigation systems against all types of security attacks. This paper introduces a novel approach, leveraging digital twin technology, for simulating security attacks in smart irrigation systems. Towards achieving this goal, a digital twin, mirroring a physical irrigation system has been built, and a simulation model has been included into it to mimic the perception layer and personal area network attacks. Such a digital twin helps in what-if analysis, to understand the impact of these attacks, on the performance of smart irrigation system. An Amazon Web Services (AWS) based prototype, combining real-time data from sensors with simulation model of the impact of potential security breaches, has been successfully developed. Further, how the digital twin can be used in detecting intrusions, thus enhancing the overall resilience and security of smart irrigation systems, has been presented. This work contributes to the growing field of cybersecurity for IoT applications, specifically addressing the unique challenges posed by the security attacks in smart irrigation. Β© 2024 IEEE.
publications-4916 Conference paper 2024 Fang C.; Peng J.; Yang L.; Shen J.; Zhang D.; Wang J. Parameterized Water Body Twin Model Construction Research 2024 6th International Conference on Communications, Information System and Computer Engineering, CISCE 2024 10.1109/CISCE62493.2024.10653123 With the continuous development of digital twin technology, it has been widely applied in various fields. The application of digital twin technology in the water environment is becoming a hot topic, bringing new possibilities for water quality monitoring and early warning. Digital twin models are the core elements of digital twins. Currently, existing digital twin models of water bodies are mainly implemented in a static manner, making it difficult to express the dynamic changes of water bodies, integrate with environmental factors, and achieve realistic water twin scenarios. This paper proposes the construction of water body twin models through parameterization, and by integrating environmental parameters, accurately and intuitively expresses the characteristics and dynamic changes of water bodies. This has reference significance for promoting the development of digital twin technology. Β© 2024 IEEE.
publications-4917 Conference paper 2024 Suquet R.R.; Nguyen T.H.; Ricci S.; Piacentini A.; Bonassies Q.; Sadki M.; Fatras C.; Lavergne E.; Gaudissart V.; Guzzonato E.; Prugniaux M.; Froidevaux A.; Aouassar O.; Valladeau G.; Poisson J.C.; Huang T.; Bretar F. The SCO-Flooddam Digital Twin Project: A Pre-Operational Demonstrator for Flood Detection, Mapping, Prediction and Risk Impact Assessment International Geoscience and Remote Sensing Symposium (IGARSS) 10.1109/IGARSS53475.2024.10640907 As a result of climate change, extreme hydrometeorological events are becoming increasingly frequent. Over the past 20 years, more than 2 billion people have been exposed to consequences of fluvial floods. Flood detection, rapid mapping and risk assessment products play an important role in flood emergency response and management. Within this context, FloodDAM-Digital Twin is a pre-operational prototype which provides an automated service to reliably detect, monitor, assess and predict floods at local and global scale within digital twin Franco-American collaboration. At the end of the project, a proof of concept demonstration will be realized over French and USA selected catchments. This prototype could be commercialized for both public and private entities in the field of water management and risk prevention. The work presented in this paper relies on scientific improvements for each product and services as well as on the digital Twin architecture that allows interoperability with other systems. Β© 2024 IEEE.
publications-4918 Conference paper 2024 Zhou L.; Song C.; Wang X.; Chen X.; Du W. Analysis of working characteristics of five-cylinder reciprocating pump based on digital twin and development of monitoring platform Proceedings - 2024 39th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2024 10.1109/YAC63405.2024.10598526 Water injection development is the main development mode of Shengli Oilfield. Reciprocating pump is the key equipment to increase injection pressure, but there are problems such as pump valve damage, plunger wear and so on. Aimed at the problem of stopping the reciprocating pump for maintenance due to the sudden failure of the pump due to the failure to effectively monitor the operating conditions of the pump, according to the design parameters of the on-site five cylinder single acting reciprocating pump, the How and pressure mathematical models were established through MATLAB analysis software and the accuracy verification was completed; The unit real-time content development platform software is used to establish the dynamic 3D model of the reciprocating pump, and the virtual entity model of the water injection pump is created in a digital way, so as to develop a reciprocating pump status monitoring platform based on the digital twin technology; Through the simulation operation of the digital twin of the reciprocating pump, combined with the actual common faults, the pressure and How data under the fault condition are obtained, the actual and theoretical working flow pressure data of the reciprocating pump are analyzed, the monitoring of the operating condition of the reciprocating pump is realized, and the whole life cycle process of the simulation and prediction of the pump unit is realized. The research content is of great significance to ensure the long-term, safe, efficient and stable production of oilfield water injection system, reduce maintenance costs and improve economic benefits of oilfield development. Β© 2024 IEEE.
publications-4919 Conference paper 2024 Halla A.; Jaakkola S.; Tupi R.; Linna P. Collaborative Data Collection in Agriculture - Case Sub-Irrigation On-Farm Experiment 2024 47th ICT and Electronics Convention, MIPRO 2024 - Proceedings 10.1109/MIPRO60963.2024.10569347 Agriculture is increasingly data-intensive. As farmers aim for more informed decisions, they produce growing data volumes that are valuable for the wider farm data ecosystem, particularly in research. Data collection in on-farm experiments benefits the farmer by improving confidence in the experiment results but also provides researchers with data from production-scale environments.Artificial drainage systems can be used for sub-irrigation of crops in open-field farming, in addition to their original use. The effectiveness of this kind of irrigation depends on local soil characteristics and natural groundwater level patterns, necessitating on-site measurements. However, developing a monitoring system required for validating the effect of sub-irrigation can be out of reach of an individual farmer.Over three years, a system for this purpose was collaboratively designed and built during a split-field experiment. This collaboration included the farmer, researchers, extension practitioners and companies. The system proved effective in validating the irrigation's impact on groundwater levels. The experiment helped develop collaboration in the region and provided insight into the requirements and challenges of developing a farm data ecosystem. The system itself provides a basis for long-term monitoring and supports further research, including the use of the data in simulation and AI models for predictive analytics and optimization. Β© 2024 IEEE.
publications-4920 Book chapter 2024 Wangere J.; Sinde R.; Ally M.; Mbwambo O. A Digital Twin Approach and Challenges for Real-Time Automated Surface-Drip Irrigation Monitoring: A Case of Arusha Tanzania Progress in IS 10.1007/978-3-031-56603-5_5 Irrigation monitoring has become an inevitable tool for conserving water, which is a scarce resource. As weather patterns become more unpredictable, with sporadic rainfall, drought is proving to be a more common phenomenon. Despite efforts by researchers within the confines of the fourth industrial revolution to propose methods for automating irrigation systems, there is a need to catch up with the latest technological trends that combine a multifaceted approach for integrating smart technologies with human interaction and intellect to enhance accuracy in providing a real-time experience for remote monitoring and control of irrigation systems. Moreso, as the world shifts into the fifth industrial revolution. This paper provides an overview of digital twin technology and proposes how it can be adopted in the development of a real-time and automated irrigation monitoring system. For practicality, the proposed digital twin can be incorporated into an existing manual irrigation system thus minimizing the need for complete overhaul of pre-existing manual systems, and keeping the cost of implementation within affordable range for low-income markets. The challenges that face digital twin implementation for irrigation automation have also been highlighted with possible remedies. Β© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.