Despite progresses in representing different processes, hydrological models remain uncertain. Their uncertainty stems from input and calibration data, model structure, and parameters. In ...characterizing these sources, their causes, interactions and different uncertainty analysis (UA) methods are reviewed. The commonly used UA methods are categorized into six broad classes: (i) Monte Carlo analysis, (ii) Bayesian statistics, (iii) multi-objective analysis, (iv) least-squares-based inverse modeling, (v) response-surface-based techniques, and (vi) multi-modeling analysis. For each source of uncertainty, the status-quo and applications of these methods are critiqued in gauged catchments where UA is common and in ungauged catchments where both UA and its review are lacking. Compared to parameter uncertainty, UA application for structural uncertainty is limited while input and calibration data uncertainties are mostly unaccounted. Further research is needed to improve the computational efficiency of UA, disentangle and propagate the different sources of uncertainty, improve UA applications to environmental changes and coupled human–natural-hydrologic systems, and ease UA’s applications for practitioners.
Featured Cover Wan, Hua‐Ping; Zhang, Zi‐Nan; Luo, Yaozhi ...
International journal for numerical methods in engineering,
12/2022, Volume:
123, Issue:
24
Journal Article
Peer reviewed
Open access
The cover image is based on the Research Article Analytical uncertainty quantification approach based on adaptive generalized co‐Gaussian process model by Hua‐Ping Wan et al., ...https://doi.org/10.1002/nme.7102.
The uncertain variable is used to model a quantity under human uncertainty, and the weighted Formula Omitted-out-of-Formula Omitted system is used to model a system of Formula Omitted components, ...which functions if and only if the total weights of functioning components is greater than Formula Omitted. Considering the human uncertainty in operating the system, this paper introduces the uncertain variable to the weighted Formula Omitted-out-of-Formula Omitted system, and proposes a concept of uncertain weighted Formula Omitted-out-of-Formula Omitted system. Some formulas are derived to calculate the reliability index of such a system. As a generalization, this paper also studies an uncertain weighted Formula Omitted-out-of-Formula Omitted system whose weights are estimated by experts and modeled by uncertain variables instead of crisp numbers. In addition, this paper analyzes the importance measure of the components in the uncertain weighted Formula Omitted-out-of-Formula Omitted systems.
Abstract
This article presents a novel design of a temperature calibration device and calibration procedure for refrigerated centrifuge. The temperature calibration results of 8 typical types of ...refrigerated centrifuges and the uncertainty analysis shows that the calibration device and calibration procedure presented in this article can meet the requirements of the temperature calibration for refrigerated centrifuge and ensure the temperature reliability of refrigerated centrifuge in its daily use.
•The interception rates of forest canopies at different altitudes and the process of model construction exhibit variations.•The evaluation of parameter sensitivity based on the Nash-Sutcliffe ...Efficiency (NSE) and the analysis of the tuning process significantly contribute to the high efficiency of the model.•For the event-scale modeling of canopy interception in Qinghai spruce forests, the reformulated Liu model is recommended.
The study of physically-based rainfall interception is crucial for comprehending the water balance within forest ecosystems and the contribution of vegetation to the hydrological cycle, particularly in arid/semi-arid ecosystems. Despite its importance, there is a lack of comprehensive sensitivity analysis and parameter optimization, resulting in uncertain or suboptimal predictive accuracy. To mitigate these shortcomings, this research involved the establishment and assessment of three quintessential forest canopy interception models namely, the power Návar model, the reformulated Gash model, and the Liu model, within semi-arid forest environments at two different elevations. A global sensitivity analysis conducted on the three physical models indicated that the canopy saturation point and the mean rainfall intensity required for canopy saturation were the parameters to which the reformulated Gash and Liu models were most sensitive when applied to high-altitude settings. Conversely, for the Návar model, the most sensitive parameters were the interception coefficient of the linear equation, and the parameters of the power equation k and c. The quantification indices of model sensitivity exert a certain influence on the ranking of parameter sensitivities. However, for models with a limited number of parameters, the impact of these results is constrained. Conversely, the identification and utilization of characteristics specific to the parameter tuning process can significantly enhance the efficiency of model calibration. The three models employed by the research institute have all demonstrated commendable performance in modeling the canopy interception process of subalpine P. crassifolia in arid, high-altitude regions, achieving a "good" rating with Nash-Sutcliffe Efficiency values exceeding 0.7. In practical applications, we recommend giving priority to the use of the Liu model. The findings of this study provide a reference for model selection, sensitivity analysis, parameter calibration, and model evaluation in the context of extensive canopy interception modeling in arid areas with significant altitudinal variation. This constitutes an important theoretical support for the refined modeling of hydrological processes in high-altitude forests within arid zones.
The key challenge for household load forecasting lies in the high volatility and uncertainty of load profiles. Traditional methods tend to avoid such uncertainty by load aggregation (to offset ...uncertainties), customer classification (to cluster uncertainties) and spectral analysis (to filter out uncertainties). This paper, for the first time, aims to directly learn the uncertainty by applying a new breed of machine learning algorithms-deep learning. However, simply adding layers in neural networks will cap the forecasting performance due to the occurrence of over-fitting. A novel pooling-based deep recurrent neural network is proposed in this paper which batches a group of customers' load profiles into a pool of inputs. Essentially the model could address the over-fitting issue by increasing data diversity and volume. This paper reports the first attempts to develop a bespoke deep learning application for household load forecasting and achieved preliminary success. The developed method is implemented on Tensorflow deep learning platform and tested on 920 smart metered customers from Ireland. Compared with the state-of-the-art techniques in household load forecasting, the proposed method outperforms ARIMA by 19.5%, SVR by 13.1% and classical deep RNN by 6.5% in terms of RMSE.
•Operational results are presented for a pilot Fresnel SHIP plant to produce steam.•The system's dynamics are represented with sampling rate of 10 and 60 s.•An uncertainty analysis is conducted on ...the monitoring measurement equipment.•Improvement of monitoring system to reduce the heat uncertainty from 12 % to 3 %.•Detailed analysis of the system dynamics is performed to understand its behaviour.
Solar heat for industrial processes (SHIP) is an increasingly interest option as a key strategy to decarbonise the industrial sector. Linear Fresnel Collectors are a promising technology due to their cost-effectiveness and efficiency. As part of the SOLPINVAP project, an experimental facility was constructed to prove the use of modular Linear Fresnel collectors for steam production in industrial processes. SHIP plants commonly use standard industrial sensors, which can result in high uncertainty when measuring absorbed and produced heat. This can be problematic when selling energy to customers through an energy purchase agreement. To address this issue, an uncertainty analysis was conducted in the experimental plant, and more precise instrumentation was added to the installation. This reduced the uncertainty in the absorbed energy in the solar field from 12 % to 3 % and obtaining an uncertainty of 3 % in the generated energy. Additionally, the operation of the plant was studied through experimental tests conducted during the summer of 2022, and the system dynamics were analysed during the period of peak heat absorption.
Abstract
Despite being classified today as a secondary reference point of temperature, the ice point (0.0 °C) is still very important in many thermometry applications due to its simplicity, ...effectiveness, reproducibility and low cost. Uncertainties as low as some hundreds of microkelvin can be achieved, but it depends on the level of care of the experimental procedure practiced by the laboratory. The Thermometry Laboratory of Inmetro (Later) has adopted for a long time a conservative value available in literature for the uncertainty of the ice point. In order to reach more realistic results, in line with the specific equipment and experimental procedure of the lab, a study on the reproducibility of the ice point realization was conducted, enabling a significant reduction of the uncertainty previously adopted. In addition, from the uncertainty analysis performed, it was possible to show the great importance of the standardization of the ice bath preparation procedure for the achievement of better results.
We present a joint cosmological analysis of weak gravitational lensing observations from the Kilo-Degree Survey (KiDS-1000), with redshift-space galaxy clustering observations from the Baryon ...Oscillation Spectroscopic Survey (BOSS) and galaxy-galaxy lensing observations from the overlap between KiDS-1000, BOSS, and the spectroscopic 2-degree Field Lensing Survey. This combination of large-scale structure probes breaks the degeneracies between cosmological parameters for individual observables, resulting in a constraint on the structure growth parameter
S
8
= σ
8
√(Ω
m
/0.3) = 0.766
−0.014
+0.020
, which has the same overall precision as that reported by the full-sky cosmic microwave background observations from
Planck
. The recovered
S
8
amplitude is low, however, by 8.3 ± 2.6% relative to
Planck
. This result builds from a series of KiDS-1000 analyses where we validate our methodology with variable depth mock galaxy surveys, our lensing calibration with image simulations and null-tests, and our optical-to-near-infrared redshift calibration with multi-band mock catalogues and a spectroscopic-photometric clustering analysis. The systematic uncertainties identified by these analyses are folded through as nuisance parameters in our cosmological analysis. Inspecting the offset between the marginalised posterior distributions, we find that the
S
8
-difference with
Planck
is driven by a tension in the matter fluctuation amplitude parameter,
σ
8
. We quantify the level of agreement between the cosmic microwave background and our large-scale structure constraints using a series of different metrics, finding differences with a significance ranging between ∼3
σ
, when considering the offset in
S
8
, and ∼2
σ
, when considering the full multi-dimensional parameter space.
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