Search results for "uncertainty analysis"
showing 10 items of 91 documents
Systematic and statistical uncertainties of the hilbert-transform based high-precision FID frequency extraction method.
2021
Abstract Pulsed nuclear magnetic resonance (NMR) is widely used in high-precision magnetic field measurements. The absolute value of the magnetic field is determined from the precession frequency of nuclear magnetic moments. The Hilbert transform is one of the methods that have been used to extract the phase function from the observed free induction decay (FID) signal and then its frequency. In this paper, a detailed implementation of a Hilbert-transform based FID frequency extraction method is described, and it is briefly compared with other commonly used frequency extraction methods. How artifacts and noise level in the FID signal affect the extracted phase function are derived analytical…
A Plot-scale uncertainty analysis of saturated hydraulic conductivity of a clay soil
2021
Abstract Simulating soil hydrological processes at the plot or field scale requires using spatially representative values of the saturated soil hydraulic conductivity, Ks. Sampling campaigns should yield a reliable mean of Ks with a sustainable workload since measuring Ks at many points is challenging. Uncertainty analysis can be used to determine the lowest number of measurements that yield a mean Ks value with a specified accuracy level. Potential and limitations of this analysis were tested in this investigation for different extents of the sampled area and sampling densities. A clay soil was sampled intensively on two plots (plot area = 44 m2), two dates and using both small (0.15 m in …
A PERFORMANCE-BASED TOOL FOR PRIORITISING WATER METER SUBSTITUTION IN A URBAN DISTRIBUTION NETWORK
2011
User water consumption is usually measured by volumetric water meters. Water meters also provide indispensable data used by the utilities for issuing bills, obtaining the system water balance, identifying failures in the network, water theft and anomalous user behaviour. Therefore, the utilities rely on such instruments for the technical and economic management of water systems. Despite their importance, water meters are characterised by relevant intrinsic errors that are responsible for a part of so-called apparent losses, i.e. water volumes reaching a final user without being accounted for. The aim of this paper is to provide a water meter management tool that analyses the meters performa…
Retrieval of atmospheric CH4profiles from Fourier transform infrared data using dimension reduction and MCMC
2016
We introduce an inversion method that uses dimension reduction for the retrieval of atmospheric methane (CH4) profiles. Uncertainty analysis is performed using the Markov chain Monte Carlo (MCMC) statistical estimation. These techniques are used to retrieve CH4 profiles from the ground-based spectral measurements by the Fourier Transform Spectrometer (FTS) instrument at Sodankyla (67.4 degrees N, 26.6 degrees E), Northern Finland. The Sodankyla FTS is part of the Total Carbon Column Observing Network (TCCON), a global network that observes solar spectra in near-infrared wavelengths. The high spectral resolution of the data provides approximately 3 degrees of freedom about the vertical struc…
The influence of the prior distribution on the uncertainty analysis assessment of an urban drainage stormwater quality model
2009
A practical approach to improve the statistical performance of surface water monitoring networks
2019
The representativeness of aquatic ecosystem monitoring and the precision of the assessment results are of high importance when implementing the EU’s Water Framework Directive that aims to secure a good status of waterbodies in Europe. However, adapting monitoring designs to answer the objectives and allocating the sampling resources effectively are seldom practiced. Here, we present a practical solution how the sampling effort could be re-allocated without decreasing the precision and confidence of status class assignment. For demonstrating this, we used a large data set of 272 intensively monitored Finnish lake, coastal, and river waterbodies utilizing an existing framework for quantifying…
Emulation of 2D Hydrodynamic Flood Simulations at Catchment Scale Using ANN and SVR
2021
Two-dimensional (2D) hydrodynamic models are one of the most widely used tools for flood modeling practices and risk estimation. The 2D models provide accurate results
Daily streamlow prediction with uncertainty in ephemeral catchments using the GLUE methodology
2009
Abstract The Generalised Likelihood Uncertainty Estimation (GLUE) approach is presented here as a tool for estimating the predictive uncertainty of a rainfall–runoff model. The GLUE methodology allows to recognise the possible equifinality of different parameter sets and assesses the likelihood of a parameters set being acceptable simulator when model predictions are compared to observed field data. The results of the GLUE methodology depend greatly on the choice of the likelihood measure and on the choice of the threshold which determines if a parameters set is behavioural or not. Moreover the sampling size has a strong influence on the uncertainty assessment of the response of a rainfall–…
Measurement of Simplified Single- And Three-Phase Parameters for Harmonic Emission Assessment Based on IEEE 1459-2010
2021
This article investigates the feasibility of using a simplified approach, based on the measurement of power ratio parameters, for harmonic emissions assessment at the point of common coupling (PCC). The proposed approach comes from the common concept of power factor correction and the definitions of the IEEE Std. 1459-2010, where line utilization and harmonic pollution levels are evaluated by means of ratios between the power quantities of the apparent power decomposition. In addition to the IEEE Std. 1459–2010 indicators, in this article, the behavior is studied of additional parameters that are conceptually similar to those defined by the IEEE Std. 1459-2010. The suitability of such param…
Uncertainty analysis of gross primary production upscaling using Random Forests, remote sensing and eddy covariance data
2015
Abstract The accurate quantification of carbon fluxes at continental spatial scale is important for future policy decisions in the context of global climate change. However, many elements contribute to the uncertainty of such estimate. In this study, the uncertainties of eight days gross primary production (GPP) predicted by Random Forest (RF) machine learning models were analysed at the site, ecosystem and European spatial scales. At the site level, the uncertainties caused by the missing of key drivers were evaluated. The most accurate predictions of eight days GPP were obtained when all available drivers were used (Pearson's correlation coefficient, ρ ~ 0.84; Root Mean Square Error (RMSE…