1,721,039 research outputs found
Integrating Qualitative Flow Observations in a Lumped Hydrologic Routing Model
No full textThis study aims at proposing novel approaches for integrating qualitative flow observations in a lumped hydrologic routing model and assessing their usefulness for improving flood estimation. Routing is based on a three-parameter Muskingum model used to propagate streamflow in five different rivers in the United States. Qualitative flow observations, synthetically generated from observed flow, are converted into fuzzy observations using flow characteristic for defining fuzzy classes. A model states updating method and a model output correction technique are implemented. An innovative application of Interacting Multiple Models, which use was previously demonstrated on tracking in ballistic missile applications, is proposed as state updating method, together with the traditional Kalman filter. The output corrector approach is based on the fuzzy error corrector, which was previously used for robots navigation. This study demonstrates the usefulness of integrating qualitative flow observations for improving flood estimation. In particular, state updating methods outperform the output correction approach in terms of average improvement of model performances, while the latter is found to be less sensitive to biased observations and to the definition of fuzzy sets used to represent qualitative observations.Water Resource
Session HS6.1 "Hydroinformatics: computational intelligence and technological developments in water science applications", EGU General Assembly 2008 - Vienna, Austria, 13 – 18 April 2008.
No full textHydrological Sciences Programme Session HS6.1: Hydroinformatics: computational intelligence and technological developments in water science applications
Hydroinformatics has emerged over the last decade to become a recognised and established field of independent research within the hydrological sciences. Hydroinformatics is concerned with the development and hydrological application of mathematical modelling, information technology, systems science and computational intelligence tools. It provides the computer-based decision-support systems that are now entering more and more into the offices of consulting engineers, water authorities and government agencies.
The aim of this session is to provide an active forum in which to demonstrate and discuss the integration and appropriate application of emergent computational technologies in a hydrological modelling context. Topics of interest are expected to cover a broad spectrum of theoretical and practical activities that would be of interest to hydro-scientists and water-engineers. The main topics will address the following classes of methods and technologies:
• Predictive models based on the methods of computational intelligence: neural networks, fuzzy systems, support vector machines, genetic programming, cellular automata, chaos theory, etc.
• Methods for the analysis of complex data sets: principal and independent component analysis, feature extraction, data-infilling, information theory, etc.
• Optimization methods associated with heuristic search procedures: various types of genetic and evolutionary algorithms, randomized and adaptive search, ant colony and particle swarm optimization, etc.
• Hybrid modelling involving different types of models – both process-based and data-driven;
• Novel methods of analysing model uncertainty;
• Appropriate software architectures for linking different types of models.
Applications could belong to any area of hydrology or water resources: rainfall-runoff modelling, flow forecasting, sedimentation modelling, analysis of meteorological and hydrologic data sets, linkages between numerical weather prediction and hydrologic models, model calibration, model uncertainty, optimization of water resources, etc.
This is the fourth session on Hydroinformatics at EGU Assemblies and each of them was a success. On the basis of the best papers an edited volume and several Special Issues of peer-reviewed journals were initiated
Hydroinformatics: Computational intelligence and technological developments in water science applications
No full textNested algorithms for optimal reservoir operation and their embedding in a decision support platform
No full textReservoir operation is a multi-objective optimization problem traditionally solved with dynamic programming (DP) and stochastic dynamic programming (SDP) algorithms. The thesis presents novel algorithms for optimal reservoir operation named nested DP (nDP), nested SDP (nSDP), nested reinforcement learning (nRL) and their multi-objective (MO) variants correspondingly MOnDP, MOnSDP and MOnRL. The novel idea is to include a nested optimization algorithm into each state transition that reduces the initial problem dimension and alleviates the curse of dimensionality. These algorithms can solve multi-objective optimization problems, without significantly increasing the algorithm complexity, the computational expenses and can handle dense and irregular variable discretization. All algorithms are coded in Java and tested on the case study of Knezevo reservoir in the Republic of Macedonia. Nested optimization algorithms are embedded in a cloud application platform for water resources modeling and optimization. The platform is available 24X7, accessible from everywhere, scalable, distributed, interoperable, and it creates a real-time multiuser collaboration platform. This thesis contributes with new and more powerful algorithms for optimal reservoir operation and cloud application platform. All source code is available for public use and can be used by researchers and practitioners to advance the mentioned areas further.Water managementCivil Engineering and Geoscience
Data-driven streamflow simulation: the influence of exogenous variables and temporal resolution
No full textData-driven modelling approaches, like artificial neural networks, are particularly sensitive to the choice of input and output variables. This study focuses on the size of the temporal observation interval of input and output data in a river
flow prediction application, analysing the simulation performances when considering increasing time aggregations of different input variables. The analyses are carried out on the data registered in a medium-sized (1,050 km2) watershed located on the Apennine mountains, where hourly meteorological data and streamflow measurements are available over an 8-year period.
Four modelling approaches are considered for the prediction of river flow in the closure section: (1) without exogenous inputs, (2) with the additional input of past precipitation data, (3) with the additional input of past streamflow data measured in the upstream section, (4) with the additional input of both past precipitation and past upstream flow. For each modelling approach, using both (a) input data and output data at the same time scale and (b) input data at a temporal resolution finer than that of the output data, optimal modelling networks are identified and forecast performances
are compared. The results highlight how the simulation improves with the addition of exogenous inputs, in particular upstream flow data, and with the use of input data at a temporal resolution finer than that of the output data. The results also show how both such benefits increase for larger temporal aggregation of the forecast
Leveraging climate data at different spatial scales via machine learning to improve sub-seasonal drought predictions
Get PDFDroughts are one of the most dangerous natural hazards affecting today societies, with an economic impact amounting to over 9 billion euros per year in Europe. Drought events usually originate from a precipitation deficit, which can then cause water shortages, agricultural losses, and environmental degradation. Despite the numerous efforts and recent advances in extreme events forecasting, the sub-seasonal time scale still represents a challenging lead time for state-of-the-art hydroclimatic predictions. In this case, the reference period is short enough for the atmosphere to retain a memory of its initial conditions, but also long enough for oceanic variability to affect atmospheric circulation. However, the relative contribution of climate teleconnections and local atmospheric conditions to the genesis of total precipitation at sub-seasonal scale remains unclear.
In this work, we aim to address this gap by using Machine Learning (ML) to combine the information extracted from teleconnection patterns, global climate variables, and local atmospheric conditions to produce sub-seasonal drought forecasts. Specifically, we implemented a first ML pipeline that uses correlation maps to select relevant grids of global Sea Surface Temperature, Mean Sea Level Pressure, and geopotential height at 500 hPa from the ERA5 reanalysis dataset, which are spatially aggregated via Principal Component Analysis and combined with a set of local variables in the considered region. The second ML approach extends our analysis by explicitly considering the potential role of teleconnection patterns, including North Atlantic Oscillation, Scandinavian oscillation, East Atlantic oscillation, and El Niño Southern Oscillation, to identify different forecast models - in terms of both input variables and model parameters – for the different phases of the climate oscillations and for each month of the year. The resulting combination of global and local variables is then used as input in different ML models, including both feedforward neural networks and extreme learning machines.
Our framework is developed within the CLImate INTelligence (CLINT) project and tested in the task of predicting the total monthly precipitation in the Rijnland area (Netherlands). The resulting ML-based forecasts are then benchmarked against state-of-the-art dynamic forecast products, i.e. the ECMWF Extended Range forecasts. Our findings indicate that combining global and local climate information into ML-based forecast models significantly improves state-of-the-art drought forecast accuracy, thus representing a promising option to timely prompt anticipatory drought management measures
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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