Multiple Sources Of Uncertainty Research Articles

Optimization and multi-uncertainty analysis of best management practices at the watershed scale: A reliability-level based bayesian network approach

Best management practices (BMPs) have been widely adopted to mitigate diffuse source pollutants, and the simulated processes of its pollutant reduction effectiveness suffer from manifold uncertainties, such as watershed model parameters and climate change. We presented a novel Bayesian modeling framework for BMPs planning, integrating process-based watershed modeling and Bayesian optimization algorithm to reveal the impact of multiple uncertainties. The proposed framework was applied to a BMPs planning case study in the Erhai watershed, the seventh-largest freshwater lake in China. Firstly, priority management areas (PMAs) were identified for BMPs siting using a simulation-optimization approach. Bayesian networks were subsequently embedded to reveal the multiple uncertainty sources in the optimal planning and the reliability level (RL) is introduced to represent the probability to meet the water quality target with BMPs implementation. The results suggest that ENS of discharge and nutrients concentration simulation by LSPC are both greater than 0.5, which displays satisfactory performance. The identified PMAs account for 0.8% of the total watershed areas while contribute to more than 15% of nutrient loadings reduction. The analysis of multiple uncertainty sources reveals that precipitation is the most influential source of uncertainties in BMP effectiveness. The construction of hedgerows plays an important role in the nutrient reduction. With the improvement of the reliability levels, the cost increases sharply, indicating that the implementation of BMPs has a marginal utility. The study addressed the urgent need for effective and efficient BMPs planning by identifying PMAs and addressing multi-source uncertainties.

Data-driven modeling of beam loss in the LHC

In the Large Hadron Collider, the beam losses are continuously measured for machine protection. By design, most of the particle losses occur in the collimation system, where the particles with high oscilla-tion amplitudes or large momentum error are scraped from the beams. The particle loss level is typically optimized manually by changing control parameters, among which are currents in the focusing and defocusing magnets. It is generally challenging to model and predict losses based only on the control parameters, due to the presence of various (non-linear) effects in the system, such as electron clouds, resonance effects, etc., and multiple sources of uncertainty. At the same time understanding the influence of control parameters on the losses is extremely important in order to improve the operation and performance, and future design of accelerators. Prior work [1] showed that modeling the losses as an instantaneous function of the control parameters does not generalize well to data from a different year, which is an indication that the leveraged statistical associations are not capturing the actual mechanisms which should be invariant from 1 year to the next. Given that this is most likely due to lagged effects, we propose to model the losses as a function of not only instantaneous but also previously observed control parameters as well as previous loss values. Using a standard reparameterization, we reformulate the model as a Kalman Filter (KF) which allows for a flexible and efficient estimation procedure. We consider two main variants: one with a scalar loss output, and a second one with a 4D output with loss, horizontal and vertical emittances, and aggregated heatload as components. The two models once learned can be run for a number of steps in the future, and the second model can forecast the evolution of quantities that are relevant to predicting the loss itself. Our results show that the proposed models trained on the beam loss data from 2017 are able to predict the losses on a time horizon of several minutes for the data of 2018 as well and successfully identify both local and global trends in the losses.

Graph attention reinforcement learning with flexible matching policies for multi-depot vehicle routing problems

Multi-depot vehicle routing problem with soft time windows (MD-VRPSTW) is a valuable practical issue in urban logistics. However, heuristic methods may fail to generate high-quality solutions for massive problems instantly. Thus, this paper presents a novel reinforcement learning algorithm integrated with graph attention network (GAT-RL) to efficiently solve the problem. This method utilizes the encoder–decoder architecture to produce routes for vehicles starting from different depots iteratively. The encoder architecture employs graph attention network to mine the complex spatial–temporal correlations within time windows. Then, the decoder architecture designs fixed-order and full-pair matching policies to generate solutions. After off-line training, experiments show that this approach consistently outperforms Google OR-Tools with negligible computational time. Particularly, the robustness of the pre-trained model is validated under multiple sources of variations and uncertainties, including customer/depot numbers, vehicle capacities, and en-route traffic conditions.

Operation and Planning of Energy Hubs Under Uncertainty—A Review of Mathematical Optimization Approaches

Co-designing energy systems across multiple energy carriers is increasingly attracting attention of researchers and policy makers, since it is a prominent means of increasing the overall efficiency of the energy sector. Special attention is attributed to the so-called energy hubs, i.e., clusters of energy communities featuring electricity, gas, heat, hydrogen, and also water generation and consumption facilities. Managing an energy hub entails dealing with multiple sources of uncertainty, such as renewable generation, energy demands, wholesale market prices, etc. Such uncertainties call for sophisticated decision-making techniques, with mathematical optimization being the predominant family of decision-making methods proposed in the literature of recent years. In this paper, we summarize, review, and categorize research studies that have applied mathematical optimization approaches towards making operational and planning decisions for energy hubs. Relevant methods include robust optimization, information gap decision theory, stochastic programming, and chance-constrained optimization. The results of the review indicate the increasing adoption of robust and, more recently, hybrid methods to deal with the multi-dimensional uncertainties of energy hubs.

Hercules: Deep Hierarchical Attentive Multilevel Fusion Model With Uncertainty Quantification for Medical Image Classification

The automatic and accurate analysis of medical images (e.g., segmentation,detection, classification) are prerequisites for modern disease diagnosis and prognosis. Computer-aided diagnosis (CAD) systems empower accurate and effective detection of various diseases and timely treatment decisions. The past decade witnessed a spur in deep learning (DL)-based CADs showing outstanding performance across many health care applications. Medical imaging is hindered by multiple sources of uncertainty ranging fromnteasurement (aleatoric) errors, physiological variability, and limited medical knowledge (epistemic errors). However, uncertainty quantification (UQ) in most existing DL methods is insufficiently investigated, particularly in medical image analysis. Therefore, to address this gap, in this article, we propose a simple yet novel hierarchical attentive multilevel feature fusion model with an uncertainty-aware module for medical image classification coined <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hercules</i> . This approach is tested on several real medical image classification challenges. The proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hercules</i> model consists of two main feature fusion blocks, where the former concentrates on attention-based fusion with uncertainty quantification module and the latter uses the raw features. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hercules</i> was evaluated across three medical imaging datasets, i.e., retinal OCT, lung CT, and chest X-ray. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hercules</i> produced the best classification accuracy in retinal OCT (94.21%), lung CT (99.59%), and chest X-ray (96.50%) datasets, respectively, against other state-of-the-art medical image classification methods.

On Multi‐Model Assessment of Complex Degradation Paths: The Fate of Diclofenac and Its Transformation Products

AbstractWe present a methodology to quantify the impact of model structure and parametric uncertainty on formulations targeting biotransformation processes of Emerging Contaminants in subsurface water resources. The study is motivated by recognizing that modeling of bio‐mediated reactions of recalcitrant compounds in soil and aquifers is plagued by uncertainty. At the same time, process‐based models often require the parameterization of complex physico‐chemical processes, a situation which is exacerbated by the paucity of direct observations. Thus, assessment and formulation of modeling tools capable to balance complexity and reliability is a key challenge. The modeling strategy proposed here aims at pairing and applying a suite of quantitative tools starting from a prior diagnosis of multiple uncertainty sources and leading to parameter estimation and model selection in the presence of a limited number of observations. The methodology is illustrated through application to a multi‐step, reactive scenario involving biotransformation of the pharmaceutical diclofenac (DCF) in groundwater. Our framework includes four plausible models. These are obtained through successive simplifications of a recently developed highly complex model. Such simplifications are accomplished consistent with the results of a comprehensive Multi‐Model Global Sensitivity Analysis. The latter allows ranking the levels of influence of system processes on model outputs by incorporating the effects of model formulation and parametric uncertainties. The kinetic of the loop‐initiating process (DCF nitrosation, linked to the temporal evolution of N‐cycle components) is documented as dominating in explaining the variability of model outputs of environmental interest. Model discrimination criteria suggest that a simplified counterpart of the reference model is favored to interpret available data. Our modeling approach can assist interpretation and prototyping of a wide range of contaminant biotransformation models. The latter is a key objective also for the purpose of developing credible (environmental) risk assessment tools and designing experimental sampling campaigns.

The role of different sources of uncertainty on the stochastic quantification of subsurface discharges in heterogeneous aquifers

The quantification of subsurface discharges may significantly be affected by multiple sources of uncertainty, especially in highly heterogeneous aquifers. In this work, subsurface discharges within alluvial aquifers located in the province of Lecco (Lombardy, Northern Italy) are quantified relying upon a Monte Carlo (MC) framework under geological and conditioning data uncertainty sources. These are respectively employed through different geological conceptual models, whose facies spatial distributions are simulated through Sequential Indicator Simulations methodology (SISIM), and by conditioning stochastic facies simulations with different data constraints. Stochastic porosity fields are then achieved upon facies simulated fields by associating average porosity values to each local simulated facies. Then, stochastic subsurface discharge fluxes are quantified under steady hydraulic conditions upon saturated hydraulic fields, inferred from porosity ones. Then, the study of subsurface discharges is pursued via the Generalized Extreme Values (GEV) approach. This method enables both to interpret peak-over-thresholds (POTs) discharges spatial distribution, and to study the role geological and data uncertainty sources on their quantification, for example studying discharges conveyed towards a lake through the surrounding aquifer. These uncertainty-based analyses show that subsurface discharges clearly convey in correspondence of main alluvial aquifers, as well as the tendency to be collected along preferential pathways. Furthermore, the inspection of POTs empirical probability spatial distributions highlights that the geological uncertainty plays as a primary uncertainty source rather than the conditioning data. The latter plays a minor role, e.g. between different employed models, only in presence of a relevant geological difference between analyzed ones. These results are consistent with other kinds of information, such as hydrogeological sections and available piezometric maps, providing novel contributions to understanding how to quantify subsurface discharges in poorly monitored aquifers or how to treat various sources of uncertainty.

Multi-Metric Validation Under Uncertainty for Multivariate Model Outputs and Limited Measurements

Abstract Model validation for real-world systems involves multiple sources of uncertainty, multivariate model outputs, and often a limited number of measurement samples. These factors preclude the use of many existing validation metrics, or at least limit the ability of the practitioner to derive insights from computed metrics. This paper seeks to extend the area metric (univariate only) and the model reliability metric (univariate and multivariate) to account for these issues. The model reliability metric was found to be more extendable to multivariate outputs, whereas the area metric presented some difficulties. Metrics of different types (area and model reliability), dimensionality (univariate and multivariate), and objective (bias effects, shape effects, or both) are used together in a “multimetric” approach that provides a more informative validation assessment. The univariate metrics can be used for output-by-output model diagnosis and the multivariate metrics contributes an overall model assessment that includes correlation among the outputs. The extensions to the validation metrics in this paper address limited measurement sample size, improve the interpretability of the metric results by separating the effects of distribution bias and shape, and enhance the model reliability metric's tolerance parameter. The proposed validation approach is demonstrated with a bivariate numerical example and then applied to a gas turbine engine heat transfer model.

Drivers of uncertainty in precipitation frequency under current and future climate – application to Maryland, USA

The intensity duration frequency (IDF) curves that inform engineering design must reflect the effects of changing climate on extreme precipitation events. These changes can be addressed, in part, by statistically assessing synthetic data/outputs from high-resolution climate model projections to develop IDF curves. The estimation of IDF curves is associated with multiple sources of uncertainty. Most studies that characterize uncertainty in IDF curves under climate change only address the uncertainty due to the choice and processing of climate model outputs, but neglect uncertainty from statistical modeling choices. This study assesses the uncertainty introduced by developing IDF curves for Maryland, USA from model simulations of current and future climate, using statistical methods that are used in U.S. practice. The study analyzes output time series from the North American Regional Climate Change Assessment Program (NARCCAP) suite, which consists of 6 Atmosphere-Ocean General Circulation Models, each spatially downscaled with two different Regional Climate Models. In a separate study, the 3-hour NARCCAP model output was temporally downscaled to 15 min using two different machine learning models. Uncertainty is assessed both across and within models. Across-model uncertainty arises from the differences among synthetic time-series for precipitation and other meteorological variables produced by the 12 NARCCAP climate model projections. Within-model uncertainty arises from the modeling choices used to develop statistical IDF curves from a single climate model time series. These choices include: temporal downscaling method, time-series type, distribution, and parameter estimation method. The choice of climate model (across-model uncertainty) is the dominant contributor under both current and future climate conditions. On the within-model level, the other sources of uncertainty contribute differently for different climate models. The development and application of future-climate IDF curves should acknowledge the uncertainty introduced by statistical modeling choices, as well as by variation among climate model projections.

Projections of Future Air Quality Are Uncertain. But Which Source of Uncertainty Is Most Important?

AbstractUnderstanding how air pollution events may change in the future is of key importance to decision makers. Multi‐model intercomparison projects focusing on atmospheric chemistry and air quality have been performed to inform the latest IPCC assessments. Future anthropogenic emission changes have generally been the foci of such model experiments, envisaged as the dominant driver of future atmospheric composition. The latest model assessments such as AerChemMIP utilize multi‐model ensembles but also have limited individual model ensembles which permit different sources of uncertainty to be characterized. The recent study by Fiore et al. (2022, https://doi.org/10.1029/2021JD035985) specifically considers a multi‐model and multi‐member ensemble approach. It adds to the quantification of uncertainty in future projections through delineating uncertainty due to model diversity and due to internal or natural climate variability within the climate system, for mean and high PM2.5 air pollution events over the Eastern USA in the 21st century. Exploring the separate roles of internal climate variability and model diversity adds further value to the important research issue of quantifying how future anthropogenic climate change impacts air quality. Future multi‐model intercomparisons need to balance the additional knowledge gained from research into understanding multiple sources of uncertainty that can inform decision making vs. the resource costs of performing these experiments using Earth System Models with interactive chemistry.

A Mixed-Level Factorial Inference Approach for Ensemble Long-Term Hydrological Projections over the Jing River Basin

Abstract Long-term hydrological projections can vary substantially depending on the combination of meteorological forcing dataset, hydrologic model (HM), emissions scenario, and natural climate variability. Identifying dominant sources of model spread in an ensemble of hydrologic projections is critically important for developing reliable hydrological projections in support of flooding risk assessment and water resources management; however, it is not well understood due to the multifactor and multiscale complexities involved in the long-term hydrological projections. Therefore, a stepwise clustered Bayesian (SCB) ensemble method will be first developed to improve the performance of long-term hydrological projections. Meanwhile, a mixed-level factorial inference (MLFI) approach is employed to estimate multiple uncertainties in hydrological projections over the Jing River basin (JRB). MLFI is able to reveal the main and interactive effects of the anthropogenic emission and model choices on the SCB ensemble projections. The results suggest that the daily maximum temperature under RCP8.5 in the 2050s and 2080s is expected to respectively increase by 3.2° and 5.2°C, which are much higher than the increases under RCP4.5. The maximum increase of the RegCM driven by CanESM2 (CARM)-projected changes in streamflow for the 2050s and 2080s under RCP4.5 is 0.30 and 0.59 × 103 m s−3 in November, respectively. In addition, in a multimodel GCM–RCM–HM ensemble, hydroclimate is found to be most sensitive to the choice of GCM. Moreover, it is revealed that the percentage of contribution of anthropogenic emissions to the changes in monthly precipitation is relatively smaller, but it makes a more significant contribution to the total variance of changes in potential evapotranspiration and streamflow. Significance Statement Increasing concerns have been paid to climate change due to its aggravating impacts on the hydrologic regime, leading to water-related disasters. Such impacts can be investigated through long-term hydrological projection under climate change. However, it is not well understood what factor plays a dominant role in inducing extensive uncertainties associated with the long-term hydrological projections due to plausible meteorological forcings, multiple hydrologic models, and internal variability. The stepwise cluster Bayesian ensemble method and mixed-level factorial inference approach are employed to quantify the contribution of multiple uncertainty sources. We find that the total variance of changes in monthly precipitation, potential evapotranspiration, and streamflow can be mainly explained by the model choices. The identified dominant factor accounting for projection uncertainties is critically important for developing reliable hydrological projections in support of flooding risk assessment and water resources management. It is suggested that more reliable models should be taken into consideration in order to improve the projection robustness from a perspective of the Loess Plateau.

Model Uncertainty and Correctability for Directed Graphical Models

Probabilistic graphical models are a fundamental tool in probabilistic modeling, machine learning and artificial intelligence. They allow us to integrate in a natural way expert knowledge, physical modeling, heterogeneous and correlated data and quantities of interest. For exactly this reason, multiple sources of model uncertainty are inherent within the modular structure of the graphical model. In this paper we develop information-theoretic, robust uncertainty quantification methods and non-parametric stress tests for directed graphical models to assess the effect and the propagation through the graph of multi-sourced model uncertainties to quantities of interest. These methods allow us to rank the different sources of uncertainty and correct the graphical model by targeting its most impactful components with respect to the quantities of interest. Thus, from a machine learning perspective, we provide a mathematically rigorous approach to correctability that guarantees a systematic selection for improvement of components of a graphical model while controlling potential new errors created in the process in other parts of the model. We demonstrate our methods in two physico-chemical examples, namely quantum scale-informed chemical kinetics and materials screening to improve the efficiency of fuel cells.

Regional-scale assessment of earthquake-induced slope displacement considering uncertainties in subsurface soils and hydrogeological condition

Realistic prediction of coseismic landslides is crucial for the design of civil infrastructures and geotechnical systems in seismically active regions. Coseismic landslides are complicated nonlinear and large-deformation processes triggered by ground shaking. To date, many gaps still remain in the scientific understanding of the landslides, particularly in areas lacking information on the subsurface stratigraphy. Analytical methods for estimating coseismic landslides have been based on highly simplified models, where many key influential factors have been overlooked, leading to large uncertainty in using empirical models for landslide prediction. In addition, more evidence recently underscores the necessity of modeling coupled effects between topography and soil amplification, which gives rise to complex wave propagation patterns due to scattering and diffracting of waves within the low-velocity near-surface layers. Neglecting topographic amplification and soil layers could result in significantly underpredicted regional-scale hazards of landslides. In this study, a regional-scale coseismic landslide simulation is carried out using the integral Spectral Element Method (SEM)-Newmark method for Hong Kong Island to quantify the coupled effect of 3D topography, subsurface soil condition as well as hydrogeological conditions. By leveraging extensive geological borehole data, a rigorous statistical model that characterizes uncertainties associated with subsurface soils is established. Influence of key factors that are not well considered in previous coseismic landslide studies, such as variation in irregular rockhead and spatially distributed shear-wave velocity are quantitatively analyzed. Numerical results demonstrate that by rigorously incorporating multiple sources of subsurface soil uncertainties, the total area of landslides in the study region can increase up to 30% compared with benchmark model as evidenced in the physics-based numerical simulation. These case studies will provide a valuable opportunity to revisit key factors that influence coseismic landslides using measured field data.

Time window optimization for attended home service delivery under multiple sources of uncertainties

We consider a vehicle routing problem variant to optimize time window assignments together with vehicle routing and scheduling decisions, under the uncertainties of trip time, service time and possible customers’ cancellations. We minimize the expected cost of vehicles’ overtime, idleness, and customer waiting, when also allowing to add new customers to existing schedules. We formulate a two-stage stochastic mixed-integer programming model using finite samples of the uncertain parameters, where in the first stage, we optimize vehicle routes and assign service time windows to customers, and in the second stage, we construct a linear program to compute the resultant undesirable cost given routes and time windows. We also propose a re-optimization method and an insertion-based linear program for accommodating real-time requests in a rolling horizon way for dynamic operations. To speed up computation, we further decompose the problem into three phases and propose Assignment–Routing–Scheduling heuristics. We first design three clustering algorithms based on spatial similarities to assign customers to vehicles, and then combine the nearest-neighbor and smallest-variance rules to decide the route for each vehicle. Finally, we cast the scheduling part as a Newsvendor problem variant and apply inventory approximations to derive closed-form solutions for determining time windows. We conduct numerical studies on diverse instances generated using both well-established benchmark data sets and Ford’s mobile service data, to compare different approaches and demonstrate the benefits of allowing flexible time-window assignments.

Polynomial chaos expansion based sensitivity analysis of predicted knee reactions—assessing the influence of the primary ligaments in distraction based models

Computational knee models have shown that predicted condylar reactions are sensitive to the utilized ligament mechanical parameters. These models, however, are computationally expensive with multiple sources of uncertainty. Traditional uncertainty analysis using Monte-Carlo (MC) inspired methods are costly to perform. The purpose of this study was to use two example calibrated knee models to compare quasi-MC versus polynomial chaos expansion (PCE) sensitivity analyses of predicted condylar reactions that included uncertainty in the mechanical parameters of the ligaments. PCE was practically identical versus quasi-MC with 95% and 98% reductions in model evaluations for analyses with 10 and 6 uncertain variables, respectively.

Advancing freshwater ecological forecasts: Harmful algal blooms in Lake Erie

Ecological models help provide forecasts of ecosystem responses to natural and anthropogenic stresses. However, their ability to create reliable predictions requires forecasts with track records sufficiently long to build confidence, skill assessments, and treating uncertainty quantitatively. We use Lake Erie harmful algal blooms as a case study to help formalize ecological forecasting. Key challenges for models include uncertainty in the deterministic structure of the load-bloom relationship and the need to assess alternative drivers (e.g., biologically available phosphorus load, spring load, longer term cumulative load) with a larger dataset. We enhanced a Bayesian model considering new information and an expanded data set, test it through cross validation and blind forecasts, quantify and discuss its uncertainties, and apply it for assessing historical and future scenarios. Allowing a segmented relationship between bloom size and spring load indicates that loading above 0.15 Gg/month will have a substantially higher marginal impact on bloom size. The new model explains 84 % of interannual variability (9.09 Gg RMSE) when calibrated to the 19-year data set and 66 % of variability in cross validation (12.58 Gg RMSE). Blind forecasts explain 84 % of HAB variability between 2014 and 2020, which is substantially better than the actual forecast track record (R2 = 0.32) over this same period. Because of internal phosphorus recycling, represented by the long-term cumulative load, it could take over a decade for HABs to fully respond to loading reductions, depending on the pace of those reductions. Thus, the desired speed and endpoint of the lake's recovery should be considered when updating and adaptively managing load reduction targets.Results are discussed in the context of ecological forecasting best pactices: incorporate new knowledge and data in model construction; account for multiple sources of uncertainty; evaluate predictive skill through validation and hindcasting; and answer management questions related to both short-term forecasts and long-term scenarios.

Hierarchical Dynamic Bayesian Network-Based Fatigue Crack Propagation Modeling Considering Initial Defects.

Orthotropic steel decks (OSDs) are inevitably subjected to fatigue damage caused by cycled vehicle loads in long-span bridges. This study establishes a probabilistic analysis framework integrating the dynamic Bayesian network (DBN) and fracture mechanics to model the fatigue crack propagation considering mutual correlations among multiple fatigue details. Both the observations of fatigue crack length from field inspection and monitoring data of vehicle loads from the weight-in-motion (WIM) system are utilized. First, fracture mechanics-based uncertainty analysis is performed to determine the multiple uncertainty sources in the Paris crack propagation model, material property, and observation data of crack length. The uncertainty of monitoring data of vehicle loads is ignored because of its high accuracy; consequently, the vehicle-load-related uncertainty is spontaneously ignored, which is also demonstrated to be very small on the investigated actual bridges. Second, a hierarchical DBN model is introduced to construct mutual dependencies among various uncertainties and intra-correlations in the propagation process of multiple fatigue cracks at different components. Third, a stochastic traffic model is established based on the WIM monitoring data and multi-scale finite element analysis via substructure techniques to determine the probability distribution of vehicle-load-related parameters. After variable discretization, a refined exact inference algorithm in a forward–backward–forward manner is adopted to estimate the posterior distribution of equivalent initial crack length and update the established DBN model. Finally, the proposed method is demonstrated by a numerical case study and a typical application on an actual cable-stayed bridge with steel box girders using OSDs in China. The results show that the probability distribution of equivalent initial crack size can be spontaneously derived with a larger mean value than the results of conventional empirical analysis. Furthermore, the component-level fatigue reliability is tracked and predicted based on the established crack propagation model.

A chance-constrained optimization framework for wind farms to manage fleet-level availability in condition based maintenance and operations

Operations and maintenance (O&M) is a key contributor to wind farm expenditures. To increase competitiveness, wind farm operators are increasingly looking into leveraging real-time sensor data from condition monitoring (CM) systems. CM provides significant insights on evolving asset failure risks for wind turbines. To date, these insights have not been fully leveraged in wind farm O&M due to ad-hoc connections to decision-making. Specifically, CM applications in wind farms have been limited to detection of turbines with imminent failure risks that require immediate replacement. In reality, wind farm maintenance requires a careful proactive orchestration of O&M dependencies across turbines along with multiple sources of uncertainty associated with asset availability, operational and market conditions. This paper proposes a unified condition-based maintenance and operations scheduling approach for wind farms that models uncertainties related to turbine availability, wind power output and market price. The proposed formulation explicitly considers the turbine-to-turbine dependencies in operations and maintenance, such as opportunistic maintenance, to identify the O&M decisions that are optimal for multiple wind farms. The problem is formulated as a chance-constrained stochastic programming model to maximize operational revenue while ensuring high levels of turbine availability and generation. To make the chance constraints tractable, two approximations are proposed with a focus on sample average approximation (SAA) and prominent tail inequalities such as Markov’s inequality and Chernoff bound. Our results on a comprehensive set of experiments demonstrate that the proposed approach provides significant improvements in asset availability, market revenue and maintenance costs in large scale wind farms.

Interactive decision support and trade-off analysis for sustainable forest landscape planning under deep uncertainty

Sustainable environmental management often involves long-term time horizons and multiple conflicting objectives and, by nature, is affected by different sources of uncertainty. Many sources of uncertainty, such as climate change or government policies, cannot be addressed using probabilistic models, and, therefore, they can be seen to contain deep uncertainty. In this setting, the variety of possible future states is represented as a set of scenarios lacking any information about the likelihood of occurring. Integrating deep uncertainty into multiobjective decision support increases complexity, calling for the elaboration of appropriate methods and tools. This paper proposes a novel interactive multi-scenario multiobjective approach to support decision-making and trade-off analysis in sustainable forest landscape planning under multiple sources of uncertainty. It includes new preference simulation models aimed at reducing the decision-maker’s cognitive load and supporting the preference elicitation process. The proposed approach is applied in a case study of long-term forest landscape planning with four sustainability objectives in 12 scenarios and a forestry expert as the decision-maker. The approach is demonstrated to be efficient in exploring trade-offs in different scenarios, helping the expert gain deep insights into the problem, understand the consequences of alternative strategies, and find the most preferred robust strategy.

Simple methods for improving the communication of uncertainty in species’ temporal trends

Temporal trends in species occupancy or abundance are a fundamental source of information for ecology and conservation. Model-based uncertainty in these trends is often communicated as frequentist confidence or Bayesian credible intervals, however, these are often misinterpreted in various ways, even by scientists. Research from the science of information visualisation indicates that line ensemble approaches that depict multiple outcomes compatible with a fitted model or data may be superior for the clear communication of model-based uncertainty. The discretisation of continuous probability information into frequency bins has also been shown to be useful for communicating with non-specialists. We present a simple and widely applicable approach that combines these two ideas, and which can be used to clearly communicate model-based uncertainty in species trends (or composite indicators) to stakeholders. We also show how broader ontological uncertainty can be communicated via trend plots using risk-of-bias visualisation approaches developed in other disciplines. The techniques are demonstrated using the example of long-term plant distributional change in Britain, but are applicable to any temporal data consisting of averages and associated uncertainty measures. Our approach supports calls for full transparency in the scientific process by clearly displaying the multiple sources of uncertainty that can be estimated by researchers.