Effect Of Data Uncertainty Research Articles

Can population models be a useful tool for evaluating the status of data-deficient species?

Population and life history data remains sparse for many species, with low prospects for improvement due to the costs of research and difficulties associated with gathering field data. However, data-deficient species may nevertheless face substantial threats. Effective approaches are required to quantitatively assess threats and the effects of data uncertainty in the assessments. We explore the use of population models to assess the impact of uncertain threatening processes in the data-deficient Tasmanian masked owl Tyto novaehollandiae castanops. We addressed data-deficiency with a multi-parameter sensitivity analysis. This showed that reproductive output, adult mortality and juvenile mortality had the greatest impact on population size and growth. Using literature on a related but better studied species, the barn owl (Tyto alba), we then used population models to simulate the impacts of rodenticide exposure by increasing stochastic variation in reproductive output, adult, and juvenile mortality. We investigated rodenticide exposure because, although there is evidence of secondary poisoning of Tasmanian masked owls through consumption of contaminated prey, the effects of potentially widespread exposure on the population are unclear. The effect on population size and growth from increased stochasticity in the rodenticide sensitive parameters was negative. However, the magnitude of impacts depended on the baseline juvenile mortality rate used in each model, highlighting the importance of this uncertain parameter on our assessment of risk. Our study serves as an example of the use of population models for exploring hypothetical scenarios for data-deficient populations. Population models are an important tool for setting priorities for research and sounding a precautionary alarm where potential threats may be concealed by uncertainty.

Stochastic conformal anomaly detection and resolution for air traffic control

Safety is of the utmost importance in the air traffic system. In recent years, data-driven algorithms have emerged to identify anomalous and potentially unsafe operations based on machine learning techniques. Although many algorithms have shown notable progress in anomaly detection, they have hardly considered the fact that data can be corrupted by noise and uncertainty (e.g., navigation system error) can lead to frequent misdetection and false alarms, which could disturb air traffic controllers and result in system performance degradation. Therefore, an accurate and reliable assessment of emerging safety risks that accounts for and alleviates the effect of uncertainty in data is required for safe and efficient airspace operations. To achieve this goal, this paper proposes a conformal prediction-based framework that explicitly examines uncertainty for reliable anomaly detection and learns online from new streaming data. In addition to supporting the monitoring task of air traffic controllers, the proposed method takes one step forward and provides support for the control task, by offering a resolution strategy when anomaly probability violates the predefined threshold. The proposed method is demonstrated with real air traffic data, called automatic dependent surveillance-broadcast data.

Development of a macroscale distributed hydro-modeling method: Bayesian principal-monotonicity inference

At macroscales (e.g., large river basins, nations, continents, or the globe), hydro-hazard mitigation under climate change requires accurate, applicable hydrologic models, especially over cold regions of complicated hydroclimatic relations. As an effort to address such a challenge, we innovate a macroscale distributed hydro-modeling method, Bayesian principal-monotonicity inference (BaPMI), based on climate classification, representative-grids selection, statistical hydrology, surrogates-based Bayesian optimization, and variance-based sensitivity analyses. The method is applied to a typical large cold-region watershed, i.e., Athabasca River Basin in Canada, and reveals a series of findings such as the following representatives. BaPMI shows promising skills in quantitatively characterizing macroscale cold-region hydrology under overall impacts of heterogeneous climates. Bayesian optimization of which hyperparameters are insensitive decreases hydro-modeling computational time by up to 96.3% and, together with climate classification, largely enhances BaPMI applicability while ensuring modeling accuracies. Individual BaPMI hydro-model parameters can explain over ¾ of the variation of hydro-modeling accuracies and, due to their interaction, joint calibration is required for avoiding underestimation of the impacts. Climatic conditions dominating cross-scale uppermost hydrologic variations consist of night temperature (40%), day temperature (38%), and precipitation (22%) for all catchments; all river flows along the mainstem are associated with upstream climatic events such as glacier melts. Climatic impacts decline from upstream to downstream and from summer, spring, winter to autumn; this may relate to tempo-spatial heterogeneity of soil, vegetation, geology, human interference and other non-climatic factors. Without this study, macroscale distributed hydrologic modeling would lack one advanced method (i.e., BaPMI) that can immunize against effects of data uncertainty, subjective judgement and climatic collinearity, adapt to complicated hydroclimatic relations and diverse hydro-variable(s) distributions, reveal cross-scale uppermost hydrologic variations and, through inexpensive computations, enhance hydro-modeling accuracies to avoid climatic-impact underestimation. The findings of this study could facilitate BaPMI applications and advance macroscale (cold-region) hydrology.

Uncertainties in land use data may have substantial effects on environmental planning recommendations: A plea for careful consideration.

A key challenge of environmental planning is to craft recommendations for future sustainable spatial development amid ubiquitous uncertainties. This paper aims to explore how different data uncertainties, usually unknown to the planner, may influence environmental planning recommendations. We apply a case study-based approach, in which we provide three illustrative examples of how data with different kinds and levels of uncertainty affect environmental assessments and, by that, the decision-support provided by environmental planning. The cases stem from different spatial levels in Germany and consider 'Regional soil-based climate change mitigation' in the region of Hannover, 'State-wide habitat conservation siting' in the federal state of Saxony-Anhalt, and 'National renewable energy planning'. Based on the three examples, we discuss implications for planning practice and derive recommendations for further research. The three cases studies illustrate the substantial effects of data uncertainty on environmental assessments and planning recommendations derived from those results. We identify four problem constellations of dealing with data uncertainty in environmental planning that relate to the severeness of uncertainty impacts, the responsibility of the decision-maker, and the kinds of impacts that wrong decisions may have. We close with recommendations for further research, among others to develop robust and pragmatic methods for identifying the uncertainty levels in environmental data and assessment results.

Measuring the effects of expectations shocks

We seek to improve the measurement of the dynamic causal effects of expectation shocks by addressing issues related to data uncertainty. The expectations shocks are estimated in a mixed-frequency VAR model which incorporates monthly and quarterly economic and financial indicators. The VAR is estimated on real-time data to prevent the shocks being confounded with the effects of data uncertainty. But dynamic responses are calculated using a quarterly VAR for revised data, estimated using older vintages as instruments to account for the fact that ‘true values’ of key macroeconomic variables may never be observed. We show that expectations shocks – revisions in GDP expectations unrelated to changes in current economic fundamentals and orthogonalized to other, potentially related shocks – explain 7–8% of the two-year variation of output, investment, consumption and hours. This is similar to the proportion of business-cycle variation explained by monetary shocks, for example.

Tomographic filtering via the generalized inverse: a way to account for seismic data uncertainty

SUMMARY Tomographic-geodynamic model comparisons are a key component in studies of the present-day state and evolution of Earth’s mantle. To account for the limited seismic resolution, ‘tomographic filtering’ of the geodynamically predicted mantle structures is a standard processing step in this context. The filtered model provides valuable information on how heterogeneities are smeared and modified in amplitude given the available seismic data and underlying inversion strategy. An important aspect that has so far not been taken into account are the effects of data uncertainties. We present a new method for ‘tomographic filtering’ in which it is possible to include the effects of random and systematic errors in the seismic measurements and to analyse the associated uncertainties in the tomographic model space. The ‘imaged’ model is constructed by computing the generalized-inverse projection (GIP) of synthetic data calculated in an earth model of choice. An advantage of this approach is that a reparametrization onto the tomographic grid can be avoided, depending on how the synthetic data are calculated. To demonstrate the viability of the method, we compute traveltimes in an existing mantle circulation model (MCM), add specific realizations of random seismic ‘noise’ to the synthetic data and apply the generalized inverse operator of a recent Backus–Gilbert-type global S-wave tomography. GIP models based on different noise realizations show a significant variability of the shape and amplitude of seismic anomalies. This highlights the importance of interpreting tomographic images in a prudent and cautious manner. Systematic errors, such as event mislocation or imperfect crustal corrections, can be investigated by introducing an additional term to the noise component so that the resulting noise distributions are biased. In contrast to Gaussian zero-mean noise, this leads to a bias in model space; that is, the mean of all GIP realizations also is non-zero. Knowledge of the statistical properties of model uncertainties together with tomographic resolution is crucial for obtaining meaningful estimates of Earth’s present-day thermodynamic state. A practicable treatment of error propagation and uncertainty quantification will therefore be increasingly important, especially in view of geodynamic inversions that aim at ‘retrodicting’ past mantle evolution based on tomographic images.

Error Analysis and Model Adaptivity for Flows in Gas Networks

Abstract In the simulation and optimization of natural gas flow in a pipeline network, a hierarchy of models is used that employs different formulations of the Euler equations. While the optimization is performed on piecewise linear models, the flow simulation is based on the one to three dimensional Euler equations including the temperature distributions. To decide which model class in the hierarchy is adequate to achieve a desired accuracy, this paper presents an error and perturbation analysis for a two level model hierarchy including the isothermal Euler equations in semilinear form and the stationary Euler equations in purely algebraic form. The focus of the work is on the effect of data uncertainty, discretization, and rounding errors in the numerical simulation of these models and their interaction. Two simple discretization schemes for the semilinear model are compared with respect to their conditioning and temporal stepsizes are determined for which a well-conditioned problem is obtained. The results are based on new componentwise relative condition numbers for the solution of nonlinear systems of equations. More- over, the model error between the semilinear and the algebraic model is computed, the maximum pipeline length is determined for which the algebraic model can be used safely, and a condition is derived for which the isothermal model is adequate.

Robust network data envelopment analysis approach to evaluate the efficiency of regional electricity power networks under uncertainty.

In this paper, new Network Data Envelopment Analysis (NDEA) models are developed to evaluate the efficiency of regional electricity power networks. The primary objective of this paper is to consider perturbation in data and develop new NDEA models based on the adaptation of robust optimization methodology. Furthermore, in this paper, the efficiency of the entire networks of electricity power, involving generation, transmission and distribution stages is measured. While DEA has been widely used to evaluate the efficiency of the components of electricity power networks during the past two decades, there is no study to evaluate the efficiency of the electricity power networks as a whole. The proposed models are applied to evaluate the efficiency of 16 regional electricity power networks in Iran and the effect of data uncertainty is also investigated. The results are compared with the traditional network DEA and parametric SFA methods. Validity and verification of the proposed models are also investigated. The preliminary results indicate that the proposed models were more reliable than the traditional Network DEA model.

Predicting Early Data Revisions to U.S. GDP and the Effects of Releases on Equity Markets

The effects of data uncertainty on real-time decision-making can be reduced by predicting data revisions to U.S. GDP growth. We show that survey forecasts efficiently predict the revision implicit in the second estimate of GDP growth, but that forecasting models incorporating monthly economic indicators and daily equity returns provide superior forecasts of the data revision implied by the release of the third estimate. We use forecasting models to measure the impact of surprises in GDP announcements on equity markets, and to analyze the effects of anticipated future revisions on announcement-day returns. We show that the publication of better than expected third-release GDP figures provides a boost to equity markets, and if future upward revisions are expected, the effects are enhanced during recessions.

Achieving consensus in self-organizing multi agent systems for smart microgrids computing in the presence of interval uncertainty

Self-organizing multi agent systems, namely cooperative agent networks employing decentralized computing paradigms based on dynamic populations of mutually coupled oscillators, are assuming a major role in supporting the large-scale deployment of smart microgrids (SMGs). The adoption of these architectures would allow the agents to compute the main global variables characterizing the SMG operation without the need for a central fusion center. Thanks to this feature, all of the basic SMG control and monitoring functions could be processed according to a totally decentralized/non-hierarchical computing paradigm. Anyway, effectiveness of these architectures on real environment should face several issues not fully explored in the literature. Amongst these, the effect of data uncertainty has been recognized as one of the most critical issue to address. In trying and fixing this problem, this paper proposes new formalizations of the decentralized consensus protocols based on the use of Interval Arithmetic. The application of this reliable consensus protocols to decentralized SMG computing is explained in detail and several numerical results are presented and discussed in order to assess the effectiveness of the proposed approach.

Robust Optimization of System Design

The data of real-world optimization problems are usually uncertain, that is especially true for early stages of system design. Data uncertainty can significantly affect the quality of the nominal solution. Robust Optimization (RO) methodology uses chance and robust constraints to generate a robust solution immunized against the effect of data uncertainty. RO methodology can be applied to any generic optimization problem where one can separate uncertain numerical data from the problem's structure. Since 2000, the RO area is witnessing a burst of research activity in both theory and applications.However, RO could lead to over-conservative requirements, resulting in typical-case bad solutions or even empty solution spaces. This drawback of the classical RO methodology can be overcome by distinguishing between real decision variables and so-called state variables. While the first type should satisfy the chance or robust constraints and their value cannot depend on a specific realization of the uncertain data, the state variables are adjustable (i.e., their value can depend on the specific realization of the uncertain data), since most of the constraints defining state variables merely “calculate” their exact value, and hence are always satisfied. In this paper we summarize how adjustable RO approach can be applied to a general uncertain linear optimization problem. Then, using an allocation example we demonstrate how this approach can be integrated in the design optimization process and its impact on the optimal system design.

Effect of Data Uncertainty on Trihalomethanes Prediction in Water Supply Systems Using Kinetic Models

The present work compares the performances of first and second order kinetic models for predicting trihalomethanes (THMs) formation in a real case-study. The kinetic parameters are evaluated through an automatic calibration procedure, in which a least-squared objective function relating measured and computed residual chlorine and THMs concentrations is adopted. The effect of measurements uncertainty on the calibrated parameters and on THMs concentrations predictions are quantified in terms of confidence limits using the First Order Second Moment approach. The study reveals that the performances of the two models are quite similar, but the second order one results less influenced by uncertainty.

The effects of buffer strips and bioretention facilities on agricultural productivity and environmental quality in Central Africa

Land degradation is a growing concern in Central Africa as poor management practices continue to cause erosion and increase water runoff in agricultural fields. The implementation of best management practices (BMPs) is needed; however, productivity is often indirectly related to the environmental benefits of such practices and resource constraints often exist. The purpose of this study was to determine the effects of bioretention facilities and buffer strips on environmental quality with productivity and resources as constraints. A water quantity and quality model for an agricultural field in Central Africa was developed. Analyses were conducted to assess the marginal benefits of each BMP, the effect of different BMP combinations on environmental quality and productivity, and the effect of data uncertainty and location uncertainty on model predictions. The results showed that bioretention pits were more effective than buffer strips in increasing environmental quality. Productivity was shown to be directly related to bioretention pits, thus environmental quality can be attained without sacrificing productivity. Data uncertainties resulted in changes in the environmental quality values, but trends remained the same. Guidelines were provided to assist design engineers in developing BMP scenarios that provide the greatest productivity and environmental quality for the constraints involved. The results of this study will bring awareness to the ability of attaining environmental quality without sacrificing productivity as well as the need for accurate data in Central Africa.

Overhead line real-time rating estimation algorithm: Description and validation

This article describes research that aims to realize a real-time rating (RTR) system for power system components. The RTR technology is regarded with interest due to its potential to unlock network power transfer capacity, improve power flow congestion management flexibi-lity, and facilitate the connection of distributed generation. The solution described in this work involves the use of a limited number of meteorological stations and a series of analytical models for estimating component ratings. The effect of data uncertainty is taken into account by an estimation algorithm based on the Monte Carlo method. Estimations of conductor temperature and environmental conditions have been validated against measured data in five different network locations. Average errors of-2.2,-1.9,-1.2,-1.9, and 1.4 °C were found for the five different network locations over a period of 71 days when comparing estimates to measured results. Results analysis identified that the models used were the main source of error. The estimation of wind direction and solar radiation was the most sensitive to errors in the models. Therefore, suggestions are made regarding the improvement of these models and the RTR estimation system.

Predicting the future from observable sequences of events in astrophysics I. Maximum Likelihood Estimation for a fixed number of parameters

Using analytical and numerical methods, esti- mates are given of future predictions in astrophysics that can be gathered from a sequence of observed events, for exam- ple for -ray bursts. Some general probability considera- tions are provided and then a maximum likelihood estima- tion, together with an approximation for the large number limit of possible events. Illustrations are given of the numer- ical maximum likelihood estimation programs in the situa- tions of both a large number and a finite number of events. The effects of data uncertainty are also considered. Numer- ical calculations and comparisons with theoretical expecta- tions are presented too.

Transient tolerance analysis of power cables thermal dynamic by interval mathematic

The safeguard of power equipments is assuming a major role in the deregulated electricity market, where a malfunctioning power system could be responsible for serious damages to a large number of system operators having access to the shared energy resource. In this context thermal monitoring of power cables represents a key issue in evaluating the risks associated with a given load management policy especially during emergency conditions. In particular this demands for reliable assessment models that should be able to predict the transient thermal behaviour when the load exceeds the nameplate value. In this connection the application of both simplified and detailed power cable thermal models is often complicated by the presence of uncertainties affecting the tolerances of the input data. The effect of these uncertainties could influence the final solution to a considerable extent. A comprehensive tolerance analysis is therefore required in order to incorporate the effect of data uncertainties into the thermal modelling process. To address this problem, in the paper the employment of interval mathematic is proposed. Several numerical results are presented and discussed in order to assess the effectiveness of the proposed methodology which offer a rough qualitative insight of the solution in a very short time, comparable to the time required for a deterministic numerical simulation.

Robust Adaptive Fuzzy Identification of Time-Varying Processes with Uncertain Data. Handling Uncertainties in the Physical Fitness Fuzzy Approximation with Real World Medical Data: An Application

This study considers the problem of Robust Fuzzy approximation of a time-varying nonlinear process in the presence of uncertainties in the identification data using a Sugeno Fuzzy System while maintaining the interpretability of the fuzzy model during identification. A recursive procedure for the estimation of fuzzy parameters is proposed based on solving local optimization problem that attempt to minimize the worst-case effect of data uncertainties on approximation performance. To illustrate the approach, several simulation studies on numerical examples are provided. The developed scheme was applied to handle the vagueness, ambiguity and uncertainty inherently present in the general notion of a Medical Expert about Physical Fitness based on a set of various Physiological parameters measurements.

Stochastic modeling of heat exchanger response to data uncertainties

This paper analyses the performance of a counterflow concentric tube heat exchanger and estimates the uncertainties in the temperature response of the heat exchanging fluids caused by uncertainties in their inlet temperatures and overall heat transfer coefficients between the fluid streams. The analysis is based upon the two-point distribution technique. This paper describes the application of this technique to a heat exchanger for which the exact solution for the steady state temperature response is available. Results show that the uncertainty in inlet temperature has a stronger influence. The effect of data uncertainty in heat transfer coefficients, which generally have very high level of uncertainty, have only weak influence. The accuracy of predicted temperature response can, thus, be significantly improved by accurate measurement of the inlet fluid temperatures.

Effects of data uncertainty in Swedish critical load assessment for forest soils.

The effects of input data uncertainty on the critical loads andexceedance estimates for Swedish forest soils was assessed usingMonte Carlo simulations of the PROFILE model. The study focuseson the effects of data uncertainty on the 5%-ile critical loadat 150 × 150 km resolution and the 95%-ile exceedance at150 × 150 km and 50 × 50 km resolution.The results indicate that datauncertainty limits the possibility to differentiate grid cellson 150 × 150 km resolution. The confidence interval for agiven percentile can generally be reduced if the uncertaintiesin calculated critical loads are addressed simultaneously forall sites in a grid cell. The resulting best estimates of the5%-ile critical load were found to be lowered, therebyadvocating larger deposition reductions to comply with a givengap closure of exceedance. The results further indicate that thenumber of sites within the grid cells is important for the rangeof the confidence interval for a given percentile.Re-aggregation of exceedance estimate in 50 × 50 km gridcells showed that differentiation may be improved as compared to150 × 150 km resolution. For 70% of the grid cells on 50× 50 km resolution, the confidence interval forcalculated exceedance covers both negative and positive values.

Database uncertainty as a limiting factor in reactive transport prognosis

The effect of uncertainties in thermodynamic databases on prediction performances of reactive transport modeling of uranium (VI) is investigated with a Monte Carlo approach using the transport code TReaC. TReaC couples the transport model to the speciation code PHREEQC by a particle tracking method. A speciation example is given to illustrate the effect of uncertainty in thermodynamic data on the predicted solution composition. The transport calculations consequently show the prediction uncertainty resulting from uncertainty in thermodynamic data. A conceptually simple scenario of elution of uranium from a sand column is used as an illustrating example. Two different cases are investigated: a carbonate-enriched drinking water and an acid mine water associated with uranium mine remediation problems. Due to the uncertainty in the relative amount of positively charged and neutral solution species, the uncertainty in the thermodynamic data also infers uncertainty in the retardation behavior. The carbonated water system shows the largest uncertainties in speciation calculation. Therefore, the model predictions of total uranium solubility have a broad range. The effect of data uncertainty in transport prediction is further illustrated by a prediction of the time when eluted uranium from the column exceeds a threshold value. All of these Monte Carlo transport calculations consume large amounts of computing time.