Sensitivity analysis ≈ Sensitivity analysis
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Predictive Uncertainty Estimation via Prior Networks Open
Estimating how uncertain an AI system is in its predictions is important to improve the safety of such systems. Uncertainty in predictive can result from uncertainty in model parameters, irreducible data uncertainty and uncertainty due to …
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Hydrological data uncertainty and its implications Open
Hydrologic data are at the core of our understanding of physical hydrologic processes, our simulation models and forecasts of water resources and hazards, and our monitoring of water quantity and quality. However, hydrologic data are subje…
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Predictive Uncertainty Estimation via Prior Networks Open
Estimating how uncertain an AI system is in its predictions is important to improve the safety of such systems. Uncertainty in predictive can result from uncertainty in model parameters, irreducible data uncertainty and uncertainty due to …
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Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization Open
Three-dimensional (3-D) geological structural modeling aims to determine geological information in a 3-D space using structural data (foliations and interfaces) and topological rules as inputs. This is necessary in any project in which the…
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Understanding the time‐varying importance of different uncertainty sources in hydrological modelling using global sensitivity analysis Open
Simulations from hydrological models are affected by potentially large uncertainties stemming from various sources, including model parameters and observational uncertainty in the input/output data. Understanding the relative importance of…
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Recent Advances in Surrogate Modeling Methods for Uncertainty Quantification and Propagation Open
Surrogate-model-assisted uncertainty treatment practices have been the subject of increasing attention and investigations in recent decades for many symmetrical engineering systems. This paper delivers a review of surrogate modeling method…
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Robust Model Predictive Control of Irrigation Systems With Active Uncertainty Learning and Data Analytics Open
We develop a novel data-driven robust model predictive control (DDRMPC)\napproach for automatic control of irrigation systems. The fundamental idea is\nto integrate both mechanistic models, which describe dynamics in soil moisture\nvariati…
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Managing Catastrophic Climate Risks Under Model Uncertainty Aversion Open
We propose a robust risk management approach to deal with the problem of catastrophic climate change that incorporates both risk and model uncertainty. Using an analytical model of abatement, we show how aversion to model uncertainty influ…
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The critical role of uncertainty in projections of hydrological extremes Open
This paper aims to quantify the uncertainty in projections of future hydrological extremes in the Biala Tarnowska River at Koszyce gauging station, south Poland. The approach followed is based on several climate projections obtained from t…
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Comprehensive Uncertainty Quantification and Sensitivity Analysis for Cardiac Action Potential Models Open
Recent efforts to ensure the reliability of computational model-based predictions in healthcare, such as the ASME V&V40 Standard, emphasize the importance of uncertainty quantification (UQ) and sensitivity analysis (SA) when evaluating com…
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Identifying Uncertainty in Laser Powder Bed Fusion Additive Manufacturing Models Open
As additive manufacturing (AM) matures, models are beginning to take a more prominent stage in design and process planning. A limitation frequently encountered in AM models is a lack of indication about their precision and accuracy. Often …
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Assessing the Applicability of Uncertainty Importance Measures for Power System Studies Open
This paper critically evaluates a number of uncertainty importance measures for use in power system stability studies. Sensitivity analysis of uncertain system parameters is vital as new technologies proliferate and the total level of syst…
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ON THE SOURCES OF UNCERTAINTY IN EXCHANGE RATE PREDICTABILITY Open
In a unified framework, we examine four sources of uncertainty in exchange rate forecasting models: (i) random variations in the data, (ii) estimation uncertainty, (iii) uncertainty about the degree of time variation in coefficients, and (…
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Assessing the relative importance of parameter and forcing uncertainty and their interactions in conceptual hydrological model simulations Open
Predictions of river flow dynamics provide vital information for many aspects of water management including water resource planning, climate adaptation, and flood and drought assessments. Many of the subjective choices that modellers make …
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Calibration after bootstrap for accurate uncertainty quantification in regression models Open
Obtaining accurate estimates of machine learning model uncertainties on newly predicted data is essential for understanding the accuracy of the model and whether its predictions can be trusted. A common approach to such uncertainty quantif…
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Is Precipitation Responsible for the Most Hydrological Model Uncertainty? Open
Rainfall-runoff modeling is highly uncertain for a number of different reasons. Hydrological processes are quite complex, and their simplifications in the models lead to inaccuracies. Model parameters themselves are uncertain—physical para…
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A framework for uncertainty quantification in building heat demand simulations using reduced-order grey-box energy models Open
The sophistication of building energy performance tools has significantly increased the number of user inputs and parameters used to define energy models. There are numerous sources of uncertainty in model parameters which exhibit varied c…
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Quantifying model-structure- and parameter-driven uncertainties in spring wheat phenology prediction with Bayesian analysis Open
Recent international efforts have brought renewed emphasis on the comparison of different agricultural systems models. Thus far, analysis of model-ensemble simulated results has not clearly differentiated between ensemble prediction uncert…
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“This Is What We Don't Know”: Treating Epistemic Uncertainty in Bayesian Networks for Risk Assessment Open
Failing to communicate current knowledge limitations, that is, epistemic uncertainty, in environmental risk assessment (ERA) may have severe consequences for decision making. Bayesian networks (BNs) have gained popularity in ERA, primarily…
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Uncertainty concepts for integrated modeling - Review and application for identifying uncertainties and uncertainty propagation pathways Open
We review concepts that have been used to address uncertainty in integrated modeling. Although conceptual approaches to tackle uncertainty seem diverse, a synthesis reveals more similarities than differences in the concepts published, espe…
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Comparative Assessment of Two Global Sensitivity Approaches Considering Model and Parameter Uncertainty Open
Global Sensitivity Analysis (GSA) is key to assisting appraisal of the behavior of hydrological systems through model diagnosis considering multiple sources of uncertainty. Uncertainty sources typically comprise incomplete knowledge in (a)…
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Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting Open
A non-parametric method is applied to quantify residual uncertainty in hydrologic streamflow forecasting. This method acts as a post-processor on deterministic model forecasts and generates a residual uncertainty distribution. Based on ins…
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Revisiting “An Exercise in Groundwater Model Calibration and Prediction” After 30 Years: Insights and New Directions Open
In 1988, an important publication moved model calibration and forecasting beyond case studies and theoretical analysis. It reported on a somewhat idyllic graduate student modeling exercise where many of the system properties were known; th…
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Assessment of Prediction Uncertainty Quantification Methods in Systems Biology Open
Biological processes are often modelled using ordinary differential equations. The unknown parameters of these models are estimated by optimizing the fit of model simulation and experimental data. The resulting parameter estimates inevitab…
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Bayesian Uncertainty Integration for Model Calibration, Validation, and Prediction Open
This paper proposes a comprehensive approach to prediction under uncertainty by application to the Sandia National Laboratories verification and validation challenge problem. In this problem, legacy data and experimental measurements of di…
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A GO-FLOW and Dynamic Bayesian Network Combination Approach for Reliability Evaluation With Uncertainty: A Case Study on a Nuclear Power Plant Open
Uncertainty analyses have been considered critical analysis methods for identifying the risks in reliability evaluations. However, with multi-phase, multi-state, and repairable features, this method cannot effectively and precisely display…
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Global Sensitivity Analysis for Multiple Interpretive Models With Uncertain Parameters Open
We propose a set of new indices to assist global sensitivity analysis in the presence of data allowing for interpretations based on a collection of diverse models whose parameters could be affected by uncertainty. Our global sensitivity an…
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The parameter uncertainty inflation fallacy Open
Statistical estimation of the prediction uncertainty of physical models is typically hindered by the inadequacy of these models due to various approximations they are built upon. The prediction errors caused by model inadequacy can be hand…
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Reducing structural uncertainty in conceptual hydrological modelling in the semi-arid Andes Open
The use of lumped, conceptual models in hydrological impact studies requires placing more emphasis on the uncertainty arising from deficiencies and/or ambiguities in the model structure. This study provides an opportunity to combine a mult…
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Variance-Based Sensitivity Analysis to Support Simulation-Based Design Under Uncertainty Open
Sensitivity analysis plays a critical role in quantifying uncertainty in the design of engineering systems. A variance-based global sensitivity analysis is often used to rank the importance of input factors, based on their contribution to …