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Author |
Bollen, E.; Pagan, B.R.; Kuijpers, B.; Van Hoey, S.; Desmet, N.; Hendrix, R.; Dams, J.; Seuntjens, P. |
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Title |
A database system for querying of river networks : facilitating monitoring and prediction applications |
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A1 Journal article |
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Year  |
2021 |
Publication |
Water Science And Technology-Water Supply |
Abbreviated Journal |
Water Sci Tech-W Sup |
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Keywords |
A1 Journal article; Sustainable Energy, Air and Water Technology (DuEL) |
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Abstract |
The increasing availability of real-time in situ measurements and remote sensing observations have the potential to contribute to the optimization of water resources management. Global challenges such as climate change, intensive agriculture and urbanization put a high pressure on our water resources. Due to recent innovations in measuring both water quantity and quality, river systems can now be monitored in real time at an unprecedented spatial and temporal scale. To interpret the sensor measurements and remote sensing observations additional data for example on: the location of the measurement, upstream and downstream catchment characteristics, horizontal ellipsis are required. In this paper, we present a data management system to support flow-path related functionality for decision making and prediction modelling. Adding meta data sets and facilitating (near) real-time processing of sensor data questions are key concepts for the systems. The potential of the database framework for hydrological applications is demonstrated using different applications for the river system of Flanders. In one, the database framework is used to simulate the daily discharge for each segment within a catchment using a simple data-driven approach. The presented system is useful for numerous applications including pollution tracking, alerting and inter-sensor validation in river systems, or related networks. |
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Wos |
000729755100001 |
Publication Date |
2021-12-14 |
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Series Issue |
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Edition |
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ISSN |
1606-9749 |
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Additional Links |
UA library record; WoS full record |
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Impact Factor |
0.573 |
Times cited |
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Open Access |
OpenAccess |
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Notes |
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Approved |
Most recent IF: 0.573 |
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Call Number |
UA @ admin @ c:irua:184814 |
Serial |
7387 |
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Author |
Rezaei, M.; Seuntjens, P.; Joris, I.; Boenne, W.; Van Hoey, S.; Campling, P.; Cornelis, W.M. |
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Title |
Sensitivity of water stress in a two-layered sandy grassland soil to variations in groundwater depth and soil hydraulic parameters |
Type |
A1 Journal article |
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Year |
2016 |
Publication |
Hydrology and earth system sciences |
Abbreviated Journal |
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Volume |
20 |
Issue |
1 |
Pages |
487-503 |
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Keywords |
A1 Journal article; Sustainable Energy, Air and Water Technology (DuEL) |
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Abstract |
Monitoring and modelling tools may improve irrigation strategies in precision agriculture. We used non-invasive soil moisture monitoring, a crop growth and a soil hydrological model to predict soil water content fluctuations and crop yield in a heterogeneous sandy grassland soil under supplementary irrigation. The sensitivity of the soil hydrological model to hydraulic parameters, water stress, crop yield and lower boundary conditions was assessed after integrating models. Free drainage and incremental constant head conditions were implemented in a lower boundary sensitivity analysis. A time-dependent sensitivity analysis of the hydraulic parameters showed that changes in soil water content are mainly affected by the soil saturated hydraulic conductivity K-s and the Mualem-van Genuchten retention curve shape parameters n and alpha. Results further showed that different parameter optimization strategies (two-, three-, four- or six-parameter optimizations) did not affect the calculated water stress and water content as significantly as does the bottom boundary. In this case, a two-parameter scenario, where K-s was optimized for each layer under the condition of a constant groundwater depth at 135-140 cm, performed best. A larger yield reduction, and a larger number and longer duration of stress conditions occurred in the free drainage condition as compared to constant boundary conditions. Numerical results showed that optimal irrigation scheduling using the aforementioned water stress calculations can save up to 12-22 % irrigation water as compared to the current irrigation regime. This resulted in a yield increase of 4.5-6.5 %, simulated by the crop growth model. |
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000369668400028 |
Publication Date |
2016-01-29 |
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ISSN |
1027-5606; 1607-7938 |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Call Number |
UA @ admin @ c:irua:132259 |
Serial |
8514 |
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Author |
Van Hoey, S.; Nopens, I.; van der Kwast, J.; Seuntjens, P. |
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Title |
Dynamic identifiability analysis-based model structure evaluation considering rating curve uncertainty |
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A1 Journal article |
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Year |
2015 |
Publication |
Journal of hydrologic engineering |
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Volume |
20 |
Issue |
5 |
Pages |
04014072 |
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Keywords |
A1 Journal article; Sustainable Energy, Air and Water Technology (DuEL) |
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Abstract |
When applying hydrological models, different sources of uncertainty are present, and evaluations of model performances should take these into account to assess model outcomes correctly. Furthermore, uncertainty in the discharge observations complicates the model identification, both in terms of model structure and parameterization. In this paper, the authors compare two different lumped model structures (PDM and NAM) considering uncertainty coming from the rating curve. Limits of acceptability for the model simulations were determined based on derived uncertainty bounds of the discharge observations. The authors applied the DYNamic Identifiability Approach (DYNIA) to identify structural failure of both models and to evaluate the configuration of their structures. In general, similar model performances are observed. However, the model structures tend to behave differently in the course of time, as revealed by the DYNIA approach. Based on the analyses performed, the probability based soil storage representation of the PDM model outperforms the NAM structure. The incorporation of the observation error did not prevent the DYNIA analysis to identify potential model structural deficiencies that are limiting the representation of the seasonal variation, primarily indicated by shifting regions of parameter identifiability. As such, the proposed approach is able to indicate where deficiencies are found and model improvement is needed. |
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Wos |
000353995400002 |
Publication Date |
2014-03-06 |
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Edition |
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ISSN |
1084-0699 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Times cited |
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no |
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Call Number |
UA @ admin @ c:irua:126056 |
Serial |
7829 |
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Author |
Van Hoey, S.; Seuntjens, P.; van der Kwast, J.; Nopens, I. |
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Title |
A qualitative model structure sensitivity analysis method to support model selection |
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A1 Journal article |
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Year |
2014 |
Publication |
Journal of hydrology |
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519 |
Issue |
D |
Pages |
3426-3435 |
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Keywords |
A1 Journal article; Sustainable Energy, Air and Water Technology (DuEL) |
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Abstract |
The selection and identification of a suitable hydrological model structure is a more challenging task than fitting parameters of a fixed model structure to reproduce a measured hydrograph. The suitable model structure is highly dependent on various criteria, i.e. the modeling objective, the characteristics and the scale of the system under investigation and the available data. Flexible environments for model building are available, but need to be assisted by proper diagnostic tools for model structure selection. This paper introduces a qualitative method for model component sensitivity analysis. Traditionally, model sensitivity is evaluated for model parameters. In this paper, the concept is translated into an evaluation of model structure sensitivity. Similarly to the one-factor-at-a-time (OAT) methods for parameter sensitivity, this method varies the model structure components one at a time and evaluates the change in sensitivity towards the output variables. As such, the effect of model component variations can be evaluated towards different objective functions or output variables. The methodology is presented for a simple lumped hydrological model environment, introducing different possible model building variations. By comparing the effect of changes in model structure for different model objectives, model selection can be better evaluated. Based on the presented component sensitivity analysis of a case study, some suggestions with regard to model selection are formulated for the system under study: (1) a non-linear storage component is recommended, since it ensures more sensitive (identifiable) parameters for this component and less parameter interaction; (2) interflow is mainly important for the low flow criteria; (3) excess infiltration process is most influencing when focussing on the lower flows; (4) a more simple routing component is advisable; and (5) baseflow parameters have in general low sensitivity values, except for the low flow criteria. (C) 2014 Elsevier B.V. All rights reserved. |
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Wos |
000347589600057 |
Publication Date |
2014-10-08 |
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Edition |
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ISSN |
0022-1694 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
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Times cited |
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Open Access |
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Notes |
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Approved |
no |
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Call Number |
UA @ admin @ c:irua:123809 |
Serial |
7395 |
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Author |
Mortier, S.T.F.C.; Van Hoey, S.; Cierkens, K.; Gernaey, K.V.; Seuntjens, P.; De Baets, B.; De Beer, T.; Nopens, I. |
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Title |
A GLUE uncertainty analysis of a drying model of pharmaceutical granules |
Type |
A1 Journal article |
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Year |
2013 |
Publication |
European journal of pharmaceutics and biopharmaceutics |
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85 |
Issue |
3:b |
Pages |
984-995 |
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Keywords |
A1 Journal article; Pharmacology. Therapy; Sustainable Energy, Air and Water Technology (DuEL) |
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Abstract |
A shift from batch processing towards continuous processing is of interest in the pharmaceutical industry. However, this transition requires detailed knowledge and process understanding of all consecutive unit operations in a continuous manufacturing line to design adequate control strategies. This can be facilitated by developing mechanistic models of the multi-phase systems in the process. Since modelling efforts only started recently in this field, uncertainties about the model predictions are generally neglected. However, model predictions have an inherent uncertainty (i.e. prediction uncertainty) originating from uncertainty in input data, model parameters, model structure, boundary conditions and software. In this paper, the model prediction uncertainty is evaluated for a model describing the continuous drying of single pharmaceutical wet granules in a six-segmented fluidized bed drying unit, which is part of the full continuous from-powder-to-tablet manufacturing line (Consigma (TM), GEA Pharma Systems). A validated model describing the drying behaviour of a single pharmaceutical granule in two consecutive phases is used. First of all, the effect of the assumptions at the particle level on the prediction uncertainty is assessed. Secondly, the paper focuses on the influence of the most sensitive parameters in the model. Finally, a combined analysis (particle level plus most sensitive parameters) is performed and discussed. To propagate the uncertainty originating from the parameter uncertainty to the model output, the Generalized Likelihood Uncertainty Estimation (GLUE) method is used. This method enables a modeller to incorporate the information obtained from the experimental data in the assessment of the uncertain model predictions and to find a balance between model performance and data precision. A detailed evaluation of the obtained uncertainty analysis results is made with respect to the model structure, interactions between parameters and uncertainty boundaries. (C) 2013 Elsevier B.V. All rights reserved. |
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Wos |
000330200800019 |
Publication Date |
2013-03-29 |
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ISSN |
0939-6411 |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Notes |
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Call Number |
UA @ admin @ c:irua:114876 |
Serial |
8005 |
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