Tool For Uncertainty Analysis Research Articles

Triple Collocation-Based Uncertainty Analysis and Data Fusion of Multi-Source Evapotranspiration Data Across China

Accurate estimation of evapotranspiration (ET) is critical for understanding land-atmospheric interactions. Despite the advancement in ET measurement, a single ET estimate still suffers from inherent uncertainties. Data fusion provides a viable option for improving ET estimation by leveraging the strengths of individual ET products, especially the triple collocation (TC) method, which has a prominent advantage in not relying on the availability of “ground truth” data. In this work, we proposed a framework for uncertainty analysis and data fusion based on the extended TC (ETC) and multiple TC (MTC) variants. Three different sources of ET products, i.e., the Global Land Evaporation and Amsterdam Model (GLEAM), the fifth generation of European Reanalysis-Land (ERA5-Land), and the complementary relationship model (CR), were selected as the TC triplet. The analyses were conducted based on different climate zones and land cover types across China. Results show that ETC presents outstanding performance as most areas conform to the zero-error correlations assumption, while nearly half of the areas violate this assumption when using MTC. In addition, the ETC method derives a lower root mean square error (RMSE) and higher correlation coefficient (Corr) than the MTC one over most climate zones and land cover types. Among the ET products, GLEAM performs the best, while CR performs the worst. The merged ET estimates from both ETC and MTC methods are generally superior to the original triplets at the site scale. The findings indicate that the TC-based method could be a reliable tool for uncertainty analysis and data fusion.

Assessing the influence of land use/land cover dynamics and climate change on water resources in Upper Blue Nile, Ethiopia

ABSTRACT This study evaluates the impact of climate and land use changes on the Lake Tana Basin's hydrology, using datasets on land use, weather patterns, topography, soil characteristics, and discharge. Future climate data were obtained from Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase Five (CMIP5) and generated using the Weather Generator (LARS-WG) tool from the Long Ashton Research Station with five distinct GCMs. Land use changes were projected using the Markov chain model based on cellular automata (CA). The Soil and Water Assessment Tool (SWAT) model was used to assess changes in hydrological elements between reference and future periods, with calibration and validation ensured by the Integrated Parameter Estimation and Uncertainty Analysis Tool (IPEAT). Projections indicate a 4.9 °C increase in ensemble mean annual temperature and a 16% rise in precipitation by the end of the 21st century under Representative Concentration Pathway (RCP) 8.5. Additionally, average annual hydrological components, including water yield, soil water, percolation, lateral flow, runoff, and actual and potential evapotranspiration, are expected to increase due to combined climate and land use changes. Therefore, it is crucial to fully understand these cumulative impacts before formulating and implementing water resource management strategies in the basin.

Uncertainty of microseismic sources identification and probabilistic location in underground excavation

Microseismic (MS) source location is an integral component of MS technology and essential to understanding the rock failure mechanism and avoiding potential geological hazards in underground rock excavation. However, accurate location remains challenging owing to the complex geological conditions and unknown rock failure mechanisms. In this study, a novel location framework was developed to locate the MS source positions and their uncertainties based on probabilistic programming. Probabilistic programming was utilized to determine the coordinates of the MS source and its variation using the Markov Chain Monte Carlo (MCMC) method based on the waveform equation. A classical benchmark problem was utilized to verify and illustrate the developed framework. The developed framework can not only locate the position of the MS source but also determine its variation due to the uncertainty during the monitoring and excavation. The located MS source is in agreement with the actual positions. The results show that the developed framework is a scientific, accurate, reasonable, and promising tool for the location of MS sources. Then, the developed framework was applied to locate the position of the blasting in a practical mine. This further proved that the developed framework could locate the MS source, providing an excellent uncertainty analysis tool for underground rock excavation.

Pearson Correlation and Chi-Squared Methods Applied to the Sensitivity and Uncertainty Analysis of Hydrogen Generation During a Boiling Water Reactor STSBO Using MAAP5 and AZTUSIA

Abstract After the nuclear accident at the Fukushima Daiichi nuclear power plant, hydrogen generation has resurfaced as a key issue, with respect to its quantification in the simulation of severe accidents (SAs), because of the potential risk of deflagration or detonation, which would compromise the integrity of the plant facilities. Severe accident simulation codes may have different models to predict cladding oxidation rates and the consequent hydrogen generation. Thus, sensitivity and uncertainty analysis can be applied to determine which input variables of the code have a significant impact in the implemented models, and consequently on the overall predicted values of key figures of merit. In this work, the generation of hydrogen in the reactor core during a short-term station blackout (STSBO) is studied as a figure of merit for a boiling water reactor (BWR). The scope of the analysis is until the failure of the reactor pressure vessel (RPV). This study shows a sensitivity and uncertainty analysis through the complementary calculations of two methods: Chi-squared parameter and Pearson simple correlation coefficient (PSCC). The severe accident simulation code was MAAP 5.0.3 (Modular Accident Analysis Program, EPRI, USA) and the AZTUSIA code (AZtlan Tool for Uncertainty and SensItivity Analysis, Mexico) was used as the statistical tool for the sensitivity and uncertainty analysis. Ten uncertain parameters were chosen, of which two are MAAP models options, and the values were generated via the simple random Monte Carlo technique. The results show that the Chi-squared parameter in conjunction with correlation coefficients provides a more comprehensive understanding of the impact of both quantitative and qualitative input variables on the figures of merit.

A review on sources of uncertainties for groundwater recharge estimates: insight into data scarce tropical, arid, and semiarid regions

Abstract Successful sustainable groundwater management requires accurate information on recharge for a given aquifer system. However, recharge estimates are usually used in relative terms rather than an absolute sense. A review of available studies on groundwater recharge estimate uncertainty as well as tools for uncertainty analysis was conducted. Nonetheless, except for the handful of studies that have conducted proper uncertainty analysis, most were inclined to implement multiple methods as an indication of the range of uncertainty. The global trend indicates that considering the significant number of methods for recharge estimation, very little has been done to assess the uncertainty of each method. Therefore, more attention should be given to the individual uncertainty analysis of selected methods as much as using multiple methods recommended for investigating uncertainty. Insight from the review indicates that, when used carefully, tracer-based analysis can be effective and coupling is required for uncertainty analysis. Furthermore, spatial uncertainty due to input data could potentially be minimized by using input data from multiple sources. Better conceptualization of the hydrogeological process can reduce the uncertainty of numerical modelling. This review is limited to widely used methods and excludes uncertainty due to inappropriate method implementation and controlled experimental uncertainties.

DayCent-CUTE: A global sensitivity, auto-calibration, and uncertainty analysis tool for DayCent

DayCent-CUTE: A global sensitivity, auto-calibration, and uncertainty analysis tool for DayCent

Deterministic and Probabilistic Approaches to Model and Update Dynamic Systems

Updating simulation models, based on flight test or other data, or creating a model using only experimental data is a necessary process that can be costly and laborious. Simulation updates are necessary for high-fidelity simulations used in analysis, control development, handling qualities prediction, fleet training, etc. This paper presents methods to update aircraft models based on deterministic and probabilistic approaches. The deterministic approach is based on a collection of signal processing, statistical analysis, and maximum likelihood estimators embodied in the Algorithms to Update Simulation Parameters with Experimental Data tool. Mathematically sound and statistically rigorous techniques are used to calculate updates of current model parameters and suggest new terms capturing unmodeled dynamics, thereby improving correlation with observed vehicle response. The probabilistic approach is based on generalized polynomial chaos theory included in the Algorithms for Uncertainty Representation and Analysis (AURA) tool. The AURA tool allows the user to update probabilistic models based on experimental data, formulated as a Bayesian inference problem. This paper will present the methodology for both deterministic and probabilistic approaches and provide examples of their application to different aircraft designs, including tilt-rotor and rotary-wing unmanned air system vehicles. The results for both deterministic and probabilistic modeling approaches demonstrate excellent results. The probabilistic approach is novel in its expressive power to propagate and update probabilistic models and provides users with an intuitive and powerful modeling approach.

Uncertainty and sensitivity analysis of the ASTEC simulations results of a MBLOCA scenario in a generic KONVOI plant using the FSTC tool

This paper presents the results of uncertainty and sensitivity (U&S) analysis of Medium Break Loss-of-Coolant (MBLOCA) severe accident (SA) scenario simulations performed with the ASTEC code with a generic input deck prepared for a KONVOI-1300 nuclear power plant (NPP). The analysis was done with the in-house Fast Source Term Calculation Tool (FSTC) (currently: KArlsruhe Tool for Uncertainty and Sensitivity Analysis (KATUSA)). Part of the presented here results – analysis of MBLOCA scenario simulations up to the basemat rupture - were already shown at the ERMSAR conference in 2022. Here an extension related to the analysis of shorter MBLOCA simulations (up to 6000 s after lower head vessel failure (LHVF)) and investigation of the influence from adding correlations between uncertain input parameters is given. The analysis allows to identify which uncertain input parameters influence the release of fission products (FPs) in the containment and environment at the different stages of SA progression.

Priorities and Challenges in Methodology for Human Health Risk Assessment from Combined Exposure to Multiple Chemicals.

This paper reviews key elements in the assessment of human health effects from combined exposure to multiple chemicals taking into consideration current knowledge and challenges to identify areas where scientific advancement is mostly needed and proposes a decision-making scheme on the basis of existing methods and tools. The assumption of dose addition and estimation of the hazard index (HI) is considered as a starting point in component-based risk assessments. When, based on the generic HI approach, an unacceptable risk is identified, more specific risk assessment options may be implemented sequentially or in parallel depending on problem formulation, characteristics of the chemical group under assessment, exposure levels, data availability and resources. For prospective risk assessments, the reference point index/margin of exposure (RPI/MOET) (Option 1) or modified RPI/normalized MOET (mRPI/nMOET) (Option 2) approaches may be implemented focusing on the specific mixture effect. Relative potency factors (RPFs) may also be used in the RPI approach since a common uncertainty factor for each mixture component is introduced in the assessment. Increased specificity in the risk assessment may also be achieved when exposure of selected population groups is considered (Option 3/exposure). For retrospective risk assessments, human biomonitoring data available for vulnerable population groups (Option 3/susceptibility) may present more focused scenarios for consideration in human health risk management decisions. In data-poor situations, the option of using the mixture assessment factor (MAF) is proposed (Option 4), where an additional uncertainty factor is applied on each mixture component prior to estimating the HI. The magnitude of the MAF may be determined by the number of mixture components, their individual potencies and their proportions in the mixture, as previously reported. It is acknowledged that implementation of currently available methods and tools for human health risk assessment from combined exposure to multiple chemicals by risk assessors will be enhanced by ongoing scientific developments on new approach methodologies (NAMs), integrated approaches to testing and assessment (IATA), uncertainty analysis tools, data sharing platforms, risk assessment software as well as guideline development to meet legislative requirements.

Establishment of DeCART/MIG stochastic sampling code system and Application to UAM and BEAVRS benchmarks

In this study, a DeCART/MIG uncertainty quantification (UQ) analysis code system with a multi-correlated cross section stochastic sampling (S.S.) module was established and verified through the UAM (Uncertainty Analysis in Modeling) and the BEAVRS (Benchmark for Evaluation And Validation of Reactor Simulations) benchmark calculations. For the S.S. calculations, a sample of 500 DeCART multi-group cross section sets for two major actinides, i.e., 235U and 238U, were generated by the MIG code and covariance data from the ENDF/B-VII.1 evaluated nuclear data library. In the three pin problems (i.e. TMI-1, PB2, and Koz-6) from the UAM benchmark, the uncertainties in kinf by the DeCART/MIG S.S. calculations agreed very well with the sensitivity and uncertainty (S/U) perturbation results by DeCART/MUSAD and the S/U direct subtraction (S/U-DS) results by the DeCART/MIG. From these results, it was concluded that the multi-group cross section sampling module of the MIG code works correctly and accurately. In the BEAVRS whole benchmark problems, the uncertainties in the control rod bank worth, isothermal temperature coefficient, power distribution, and critical boron concentration due to cross section uncertainties were calculated by the DeCART/MIG code system. Overall, the uncertainties in these design parameters were less than the general design review criteria of a typical pressurized water reactor start-up case. This newly-developed DeCART/MIG UQ analysis code system by the S.S. method can be widely utilized as uncertainty analysis and margin estimation tools for developing and designing new advanced nuclear reactors.

Platform development for multi-physics coupling and uncertainty analysis based on a unified framework

The multi-physics coupled methodologies that have been widely used to analyze the complex process occurring in nuclear reactors have also been used to the R&D of numerical reactors. The advancement in the field of computer technology has helped in the development of these methodologies. Herein, we report the integration of ADPRES code and RELAP5 code into the SALOME-ICoCo framework to form a multi-physics coupling platform. The platform exploits the supervisor architecture, serial mode, mesh one-to-one correspondence and explicit coupling methods during analysis, and the uncertainty analysis tool URANIE was used. The correctness of the platform was verified through the NEACRP-L-335 benchmark. The results obtained were in accordance with the reference values. The platform could be used to accurately determine the power peak. In addition, design margins could be gained post uncertainty analysis. The initial power, inlet coolant temperature and the mass flow of assembly property significantly influence reactor safety during the rod ejections accident (REA).

A novel analysis method for vibration systems under time-varying uncertainties based on interval process model

Non-probabilistic models can quantify the uncertainties of engineering systems with limited data, which is significantly convenient and economical for engineering applications. However, a key issue in the application of non-probabilistic methods to vibration problems is how to address the time-varying uncertainties, especially when the correlation is involved. In this study, a novel analysis method based on the interval process model is proposed to obtain the dynamic response bounds of vibration systems under time-varying uncertainties, where uncertainties inherently existed in external excitations and system parameters are considered. Firstly, the interval process model is utilized for describing the uncertain-but-bounded variables, and the correlation function quantifies the correlation between the variables at different times. By solving two optimization problems, the dynamic response bounds concerning uncertain system parameters are obtained. Based on the response bounds, the first-order Taylor expansion is used to handle the uncertainties of system parameters, and a homogenization treatment is proposed to suppress the interval expansion. Thus the upper and lower bounds of the dynamic response under time-varying uncertainties can be determined. To quantify the reliability of vibration systems under time-varying uncertainties, the time-varying reliability indexes related to the proposed method are constructed. Finally, the proposed method is validated by the corresponding Monte Carlo simulation method and further applied in lunar soil coring. The findings from this study can provide an important tool for uncertainty and reliability analysis of vibration systems in practical engineering.

An interactive graphical interface tool for parameter calibration, sensitivity analysis, uncertainty analysis, and visualization for the Soil and Water Assessment Tool

The Soil and Water Assessment Tool (SWAT) is one of the most widely used and well-tested eco-hydrological models. However, parameter calibration, sensitivity analysis and uncertainty analysis remain among the most challenging tasks. Existing SWAT parameter calibration, sensitivity analysis, and uncertainty analysis tools are either commercial products or free tools with limited options. This study demonstrates an interactive graphical user interface tool in the R environment for SWAT parameter calibration, sensitivity and uncertainty analyses, and visualization, called R-SWAT. Different R functions/packages for parameter calibration, sensitivity analysis, and uncertainty analysis have been incorporated into R-SWAT. Third-party packages can be integrated into R-SWAT with minimum effort. The application of R-SWAT for a test case study demonstrates its functionalities. In general, R-SWAT (1) is a potential platform for developing and testing new sensitivity or optimization packages, and (2) promotes the understanding of hydrological processes with open-source SWAT and R.

A weighted ML-KNN based on discernibility of attributes to heterogeneous sample pairs

As a well-known multi-label classification method, the performance of ML-KNN may be affected by the uncertainty knowledge from samples. The rough set theory acts as an effective tool for data uncertainty analysis, which can identify the samples easy to cause misclassification in the learning process. In this paper, a hybrid framework by fusing rough sets with ML-KNN for multi-label learning is proposed, whose main idea is to depict easy misclassified samples by rough sets and to measure the discernibility of attributes for such samples. First, a rough set model titled NRFD_RS based on neighborhood relations and fuzzy decisions is proposed for multi-label data to find the heterogeneous sample pairs generated from the boundary regions of each label. Then, the weight of an attribute is defined by evaluating its discernibility to those heterogeneous sample pairs. Finally, a weighted HEOM distance is reconstructed and utilized to ML-KNN. Comprehensive experimental results with fourteen public multi-label data sets, including ten regular-scale and four larger-scale data sets, verify the effectiveness of the proposed framework relative to several state-of-the-art multi-label classification methods.

Evaluating the performance of advanced wells in heavy oil reservoirs under uncertainty in permeability parameters

Advanced wells play a crucial role in maximizing the efficiency of oil production. To achieve a successful design for advanced wells, several parameters must be considered and evaluated. Uncertainty in these parameters can have a significant impact on the performance assessment of the well in its lifetime. Absolute permeability, relative permeability, and permeability anisotropy are the parameters that determine reservoir permeability and are among the most important design parameters to be considered. This paper aims to assess the performance of advanced wells completed with passive and reactive downhole Flow Control Devices (FCDs) under uncertainty in the reservoir permeability parameters. The assessment is conducted through a case study on a synthetic heavy oil reservoir with a strong water drive. The EclipseSM reservoir simulator is used as a simulation tool and the Latin Hypercube Sampling (LHS) approach is applied as a sampling tool for uncertainty analysis in this study. According to the obtained results, under the presence of uncertainty, advanced wells with Autonomous Inflow Control Device (AICD) and Autonomous Inflow Control Valve (AICV) completions are able to mitigate the risk of water production by 53.20% and 71.12% respectively compared to conventional wells. However, Inflow Control Device (ICD) completion can only reduce the risk by 0.87%.

Impacts of Land-Use Change on the Hydrology of Lake Tana Basin, Upper Blue Nile River Basin, Ethiopia.

Human activities impact hydrology through changes in land use and land cover. This study examins the effects of changing land use on hydrological processes using the soil and water assessment tool (SWAT) model. The data is acquired from Landsat 4‐5 Thematic Mapper (TM) in 1989, Landsat 7 Enhanced Thematic Mapper Plus (ETM+) in 2005, and Landsat 8 Operational Land Inventory (OLI) in 2019. Image preprocessing, which includes georeferencing, radiometric and atmospheric correction, image enhancement, band composite, mosaicking, and sub‐setting, are performed. After that, supervised classification, accuracy assessment, and change detection are carried out. The hydrological changes in 1989, 2005, and 2019 are analyzed using land‐use maps. The SWAT model's calibration, validation, and sensitivity analysis are performed using the Integrated Parameter Estimation and Uncertainty Analysis Tool in the four main rivers of the basin. Farmlands and built‐up lands are found to have steadily increased in the basin, while shrublands, grasslands, and bare lands declined. Due to an expansion of agricultural and built‐up lands and a decrease in shrublands and grasslands, the basin's mean annual water yield and surface runoff increased in 2019, while evapotranspiration and lateral flow decreased compared to 1989 and 2005. Therefore, future watershed and basin management shall consider changing land use.

Improved speciation profiles and estimation methodology for VOCs emissions: A case study in two chemical plants in eastern China

Volatile organic compounds (VOCs) poses a serious health risk through not only their own toxicity but also their role as precursors of ozone and secondary organic aerosols. The chemical industry, as one of the pillar industries in eastern China, is a key source of VOCs emissions. In this study, speciated VOCs emissions were measured in two chemical plants in eastern China. Oxygenated VOCs and aromatics were found to be the dominant species categories in both plants. The ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAFP) of VOCs from dedicated resin production were both higher than general resin production. Three process-based models were used for the estimation of VOCs emissions from the two tested plants as a case study. The comparison between the emission factor model and the model with best available estimation methods (e.g., the measurement-based method, the mass balance method, the empirical formula method, and the correlation equation method) implied possible overestimation of the widely used emission factor model for the chemical industry. The probabilistic model developed in this study incorporated probability distribution of key parameters and proved to be a promising tool for emission inventory development and uncertainty analysis. The overall uncertainties of VOCs emissions based on the model were (−48%, +147%) and (−48%, +139%) for the two tested plants. In this study, the speciation profiles and estimation methodology for VOCs emissions from the chemical industry in China were both improved, which could benefit the accurate evaluation of the impacts of VOCs emissions.

Development of an integrated modeling platform for watershed simulation

The main objective of this study is to develop a new modeling platform, which integrates all necessary modeling components for watershed simulation, including a pre-processing tool, a coupling of hydrologic and river transport models, and a calibration and uncertainty analysis tool. All components are connected and immersed into an open source non-proprietary GIS software. The modeling platform is programmed in Microsoft Visual Studio (VB.Net), and equipped with a built-in GUI facility. A minimum data input requirement, full capabilities and potential applicability are most compelling of this modeling platform. The modeling platform was applied to different watersheds under distinguished weather conditions; the simulation results show not only a good agreement with the observation data, but also some improvements in comparison with the results obtained from other prevalent hydrologic models.

Modeling projected impacts of climate and land use/land cover changes on hydrological responses in the Lake Tana Basin, upper Blue Nile River Basin, Ethiopia

This study is being conducted in Lake Tana Basin, Upper Blue Nile River Basin, Ethiopia. This work focuses on the assessment of the separate and combined impacts on water balance components of both climate and LULC change. For calibration, validation and uncertainty analysis, the Soil and Water Assessment Tool (SWAT) was used in conjunction with the IPEAT (Integrated Parameter Estimation and Uncertainty Analysis Tool) package. To produce high resolution future climate data from CanESM2 GCM that could be used for impact assessment, the Statistical DownScaling Model (SDSM) was used while the future LULC prediction was generated using Cellular Automata-Markov Chain model. The hydrological response of the basin was assessed by dividing the future time periods in to 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2100) through incorporating three scenarios, such as LULC change alone, climate change alone and combined climate and LULC change. The prediction of the LULC change using the CA-Markov chain model indicates that cropland, tree cover, and built-up areas are likely to increase by 2020s, 2050s, and 2080s at the expense of grassland and shrub cover areas, leading to an increase in evapotranspiration, baseflow and streamflow conditions in the basin. By considering basin average, the climate prediction result suggests an increase in both Tmax (up to 2.14 °C) and Tmin (up to 3.2 °C) temperatures, whereas precipitation would increase by up to 25% in the basin. The results show an increase of evapotranspiration by up to 0.84%, 59.8% and 55.5% under LULC, climate and combined climate and LULC change by the end of the 21st century under RCP8.5 compared to the baseline period, respectively. Furthermore, both stream-flow and lateral flow are projected to increase by up to 12.85% (9.9%), 28.5% (20.03%) and 26.4% (29.12%) under LULC, climate and combined climate and LULC change scenarios, respectively. As predicted, the shift in magnitude in RCP8.5 emissions is greater than RCP2.6 and RCP4.5. The impacts of climate change on water balances are relatively higher than the combined effects of changes in climate and LULC. Future LULC shifts, on the other hand, change comparatively offsetting hydrological components. In order to devise local-scale adaptation and mitigation strategies, the inclusion of predicted climate and LULC change for hydrological impact studies, is therefore, very useful.

AZTUSIA: A new application software for Uncertainty and Sensitivity analysis for nuclear reactors

The AZTLAN Platform project is a Mexican national initiative which aims to have a platform for analysis and design of nuclear reactors. In order to enhance the AZTLAN Platform with Uncertainty and Sensitivity (U&S) capabilities, the AZTUSIA (AZtlan Tool for Uncertainty and SensItivity Analysis) code has been developed to perform U&S analysis.The main characteristics and capabilities of AZTUSIA are briefly described together with some verification exercises. The versatility of the AZTUSIA code has been used to analyze a transient of a BWR-5 with the SIMULATE 3-K code. An artificial perturbation has been caused for creating a more challenging case and to demonstrate the capabilities of an extended interface in which AZTUSIA can be easily adapted to other codes outside AZTLAN Platform. The U&S analysis conducted using the AZTUSIA code reflect, as expected, the impact of uncertain parameters and all capabilities of the tool are demonstrated. It is possible to assess the influence of uncertainty in the calculations and to distinguish trends and readily group and/or separate results by means of scatter and cobweb color plots. As a main conclusion, a new tool has been developed allowing U&S analysis. It is foreseen to continue increasing the tool capabilities.