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 ā–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-2681 Peer reviewed articles 2018 Thaine H. AssumpĆ§Ć£o, Ioana Popescu, Andreja Jonoski, Dimitri P. Solomatine Citizen observations contributing to flood modelling: opportunities and challenges Hydrology and Earth System Sciences 10.5194/hess-22-1473-2018 Predictive Analytics River Basins Abstract. Citizen contributions to science have been successfully implemented in many fields, and water resources is one of them. Through citizens, it is possible to collect data and obtain aĀ more integrated decision-making process. Specifically, data scarcity has always been an issue in flood modelling, which has been addressed in the last decades by remote sensing and is already being discussed in the citizen science context. With this in mind, this article aims to review the literature on the topic and analyse the opportunities and challenges that lie ahead. The literature on monitoring, mapping and modelling, was evaluated according to the flood-related variable citizens contributed to. Pros and cons of the collection/analysis methods were summarised. Then, pertinent publications were mapped into the flood modelling cycle, considering how citizen data properties (spatial and temporal coverage, uncertainty and volume) are related to its integration into modelling. It was clear that the number of studies in the area is rising. There are positive experiences reported in collection and analysis methods, for instance with velocity and land cover, and also when modelling is concerned, for example by using social media mining. However, matching the data properties necessary for each part of the modelling cycle with citizen-generated data is still challenging. Nevertheless, the concept that citizen contributions can be used for simulation and forecasting is proved and further work lies in continuing to develop and improve not only methods for collection and analysis, but certainly for integration into models as well. Finally, in view of recent automated sensors and satellite technologies, it is through studies as the ones analysed in this article that the value of citizen contributions, complementing such technologies, is demonstrated. 690900
publications-2682 Peer reviewed articles 2018 Feifei Zheng, Ruoling Tao, Holger R. Maier, Linda See, Dragan Savic, Tuqiao Zhang, Qiuwen Chen, Thaine H. AssumpĆ§Ć£o, Pan Yang, Bardia Heidari, Jƶrg Rieckermann, Barbara Minsker, Weiwei Bi, Ximing Cai, Dimitri Solomatine, Ioana Popescu Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions Reviews of Geophysics 10.1029/2018rg000616 Data Management & Analytics Natural Water Bodies AbstractData are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcingā€based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined. 690900
publications-2683 Peer reviewed articles 2017 Ciolofan, SN (Ciolofan, Sorin N.) ; Mocanu, M (Mocanu, Mariana); Cristea, V (Cristea, Valentin) CLOUD BASED LARGE SCALE MULTIDIMENSIONAL CUBIC SPLINE INTERPOLATION FOR WATER QUALITY ESTIMATION UNIVERSITY POLITEHNICA OF BUCHAREST SCIENTIFIC BULLETIN SERIES C-ELECTRICAL ENGINEERING AND COMPUTER SCIENCE Simulation & Modeling Natural Water Bodies No abstract available 690900
publications-2684 Peer reviewed articles 2018 Sorin N. Ciolofan, Aurelian Draghia, Radu Drobot, Mariana Mocanu, Valentin Cristea Decision support tool for accidental pollution management Environmental Science and Pollution Research 10.1007/s11356-017-1028-5 Simulation & Modeling Precipitation & Ecological Systems No abstract available 690900
publications-2685 Peer reviewed articles 2017 Catalin Negru, Mariana Mocanu, Valentin Cristea, Stelios Sotiriadis, Nik Bessis Analysis of power consumption in heterogeneous virtual machine environments Soft Computing 10.1007/s00500-016-2129-7 Simulation & Modeling Natural Water Bodies No abstract available 690900
publications-2686 Peer reviewed articles 2018 Dan Huru, Cristian Eseanu, Catalin leordeanu, Apostol Elena, Valentin Cristea BIGSCALE: AUTOMATIC SERVICE PROVISIONING FOR HADOOP CLUSTERS UNIVERSITY POLITEHNICA OF BUCHAREST SCIENTIFIC BULLETIN SERIES C-ELECTRICAL ENGINEERING AND COMPUTER SCIENCE Simulation & Modeling River Basins No abstract available 690900
publications-2687 Peer reviewed articles 2018 Mihaela-Andreea VASILE, Florin POP, Mihaela-Cătălina NIŢĂ, Valentin CRISTEA MLBox: Machine learning box for asymptotic scheduling Information Sciences 10.1016/j.ins.2017.01.005 Uncategorized Uncategorized No abstract available 690900
publications-2688 Peer reviewed articles 2018 Madalin Colezea, George Musat, Florin Pop, Catalin Negru, Alexandru Dumitrascu, Mariana Mocanu CLUeFARM: Integrated web-service platform for smart farms Computers and Electronics in Agriculture 10.1016/j.compag.2018.08.015 Uncategorized Uncategorized No abstract available 690900
publications-2689 Peer reviewed articles 2017 Laura Vasiliu, Florin Pop, Catalin Negru, Mariana Mocanu, Valentin Cristea, Joanna Kolodziej A Hybrid Scheduler for Many Task Computing in Big Data Systems International Journal of Applied Mathematics and Computer Science 10.1515/amcs-2017-0027 Uncategorized Wastewater Treatment Plants Abstract With the rapid evolution of the distributed computing world in the last few years, the amount of data created and processed has fast increased to petabytes or even exabytes scale. Such huge data sets need data-intensive computing applications and impose performance requirements to the infrastructures that support them, such as high scalability, storage, fault tolerance but also efficient scheduling algorithms. This paper focuses on providing a hybrid scheduling algorithm for many task computing that addresses big data environments with few penalties, taking into consideration the deadlines and satisfying a data dependent task model. The hybrid solution consists of several heuristics and algorithms (min-min, min-max and earliest deadline first) combined in order to provide a scheduling algorithm that matches our problem. The experimental results are conducted by simulation and prove that the proposed hybrid algorithm behaves very well in terms of meeting deadlines. 690900
publications-2690 Peer reviewed articles 2017 Stelios Sotiriadis, Nik Bessis, Euripides G.M. Petrakis, Cristiana Amza, Catalin Negru, Mariana Mocanu Virtual machine cluster mobility in inter-cloud platforms Future Generation Computer Systems 10.1016/j.future.2016.02.007 Data Management & Analytics River Basins No abstract available 690900