Assessment Of Model Uncertainty Research Articles

Model uncertainty assessment for symmetric and right-skewed distributions

In actuarial modeling, right-skewed distributions are of paramount importance. Typically, they have the property of becoming unimodal and symmetric after applying some increasing concave transformation T (e.g. log-transformation). In what follows, we refer to them as T -unimodal T -symmetric distributions. In this paper, we derive attainable bounds for the Value-at-Risk for such distributions when some partial information is available, such as information on the support, median, or certain moments. We also derive explicit upper and lower bounds for the Range Value-at-Risk under knowledge of unimodality, symmetry, mean, variance, and possibly bounded support. In passing, we provide a generalization of the Gauss inequality for symmetric distributions with known support. We show how these bounds improve on bounds available in the literature using a real-world automobile insurance claims dataset.

Embankment Breach Simulation and Inundation Mapping Leveraging High-Performance Computing for Enhanced Flood Risk Prediction and Assessment

Abstract. Embankment breaches represent significant hazards to communities and infrastructure, precipitating catastrophic flood occurrences. The precise prediction of floods and understanding the scope of inundation stemming from embankment failures are imperative for effective disaster preparedness and response. This research delves into a case study on simulating embankment breaches to evaluate the extent of flooding. Leveraging advanced hydrodynamic models validated through high-performance computing (HPC) systems, and integrating real-time data assimilation, we aim to improve accuracy in flood forecasting. The study endeavours to bolster flood risk management by furnishing detailed inundation maps and insights into embankment breach dynamics, thereby facilitating enhanced preparedness and response strategies. Our findings reveal that simulations conducted on multicore processors offer superior performance compared to single-core setups, yielding enhanced result accuracy and providing administrators with increased lead time. It unlocks high-resolution simulations for intricate basin details, explores a wider range of flood scenarios quickly, and allows for efficient ensemble modelling to assess model uncertainty. Through HPC utilization, we can harness high-resolution digital elevation models (DEMs) for 2D-hydrodynamic modelling, enabling rapid assessment of water spread resulting from embankment breaches within a mere 20-minute timeframe, a significant improvement from the previous 3–4 hours duration.

Development and comparison of model-integrated evidence approaches for bioequivalence studies with pharmacokinetic end points.

By applying nonlinear mixed-effect (NLME) models, model-integrated evidence (MIE) approaches are able to analyze bioequivalence (BE) data with pharmacokinetic end points that have sparse sampling, which is problematic for non-compartmental analysis (NCA). However, MIE approaches may suffer from inflation of type I error due to underestimation of parameter uncertainty and to the assumption of asymptotic normality. In this study, we developed a MIE BE analysis method that is based on a pre-defined model and consists of several steps including model fitting, uncertainty assessment, simulation, and BE determination. The presented MIE approach has several improvements compared with the previously reported model-integrated methods: (1) treatment, sequence, and period effects are only added to absorption parameters (such as relative bioavailability and rate of absorption) instead of all PK parameters; (2) a simulation step is performed to generate confidence intervals of the pharmacokinetic metrics for BE assessment; and (3) in an effort to maintain type I error, two more advanced parameter uncertainty evaluation approaches are explored, a nonparametric (case resampling) bootstrap, and sampling importance resampling (SIR). To evaluate the developed method and compare the uncertainty assessment methods, simulation experiments were performed for BE studies using a two-way crossover design with different amounts of information (sparse to rich designs) and levels of variability. Based on the simulation results, the method using SIR for parameter uncertainty quantification controls type I error at the nominal level of 0.05 (i.e., the significance level set for BE evaluation) even for studies with small sample size and/or sparse sampling. As expected, our MIE approach for BE assessment exhibited higher power than the NCA-based method, especially as the data becomes sparser and/or more variable.

Long-Term Health Outcomes of Huntington Disease and the Impact of Future Disease-Modifying Treatments: A Decision-Modeling Analysis.

Disease-modifying treatments (DMTs) such as gene therapy are currently under investigation as a potential treatment for Huntington disease (HD). Our objective was to estimate the long-term natural history of HD progression and explore the potential efficacy impacts and value of a hypothetical DMT using a decision-analytic modeling framework. We developed a health state transition model that separately analyzed 40-year-old individuals with prefunctional decline (PFD, HD Integrated Staging System [HD-ISS] stage <3, total functional score [TFC] 13), active functional decline Shoulson and Fahn category 1 (SF1, HD-ISS stage 3, TFC 13-11), and SF2 (HD-ISS stage 3, TFC 10-7). Three-year outcomes from the TRACK-HD longitudinal study were linearly extrapolated to estimate the long-term health outcomes and costs of each population. For PFD individuals, we used the HD-ISS to predict the onset of functional decline. HD costs and quality-adjusted life years (QALYs) were estimated over a lifetime horizon by applying health state-specific costs and utilities derived from a related HD burden-of-illness study. We then estimated the long-term health impacts of hypothetical DMTs that slowed or delayed onset of functional decline. We conducted sensitivity analyses to assess model uncertainties. The expected life years for 40-year-old PFD, SF1, and SF2 populations were 20.46 (95% credible range [CR]: 19.05-22.30), 13.93 (10.82-19.08), and 10.99 (8.28-22.07), respectively. The expected QALYs for PFD, SF1, and SF2 populations were 15.93 (14.91-17.44), 8.29 (6.36-11.79), and 5.79 (4.14-12.91), respectively. The lifetime costs of HD were $508,200 ($310,300 to $803,700) for the PFD population, $1.15 million ($684,500 to $1.89 million) for SF1 individuals, and $1.07 million ($571,700 to $2.26 million) for SF2 individuals. Although hypothetical DMTs led to cost savings in the PFD population by delaying the cost burdens of functional decline, they increased costs in SF1 and SF2 populations by prolonging time spent in expensive progressive HD states. Our novel HD-modeling framework estimates HD progression over a lifetime and the associated costs and QALYs. Our approach can be used for future cost-effectiveness models as positive DMT clinical trial evidence becomes available.

Estimating the economic impact of blister-packaging on medication adherence and health care costs for a Medicare Advantage health plan.

Medication nonadherence is a persistent challenge in the United States, leading to increased health care resource utilization (HCRU) and health care costs and worsened health outcomes. Medicare Star Ratings is a program developed by the Centers for Medicare and Medicaid Services (CMS) to evaluate Medicare health plan quality and performance. Three of the Medicare Part D Star Ratings quality measures assess medication adherence, showing the importance CMS places on improving medication adherence in older adults. Although a variety of medication adherence-enhancing interventions are available to help promote adherence among patients, one intervention that has shown success historically is blister-packaging. To model the potential impact of blister-packaging chronic medications on HCRU and health care costs in the Medicare population. An economic model was developed to assess the potential impact of blister-packaging the 3 Medicare Star Ratings adherence measure medication classes: renin-angiotensin system antagonists (RASAs), statins, and noninsulin antidiabetics. The model perspective was that of a hypothetical Medicare Advantage health plan with a plan size of 100,000 members. A 12-month time horizon was used in the model. The dichotomous adherence threshold in the model was set at 80% or greater of the proportion of days covered (PDC). Literature-based references were used to inform both the impact of blister-packaging on the number of patients who become adherent as well as the impact of medication adherence on HCRU and health care costs for each of the medication classes. One-way sensitivity analyses and several scenario analyses were conducted to assess model uncertainty. Owing to increased adherence from the blister-packaging intervention, the hypothetical health plan in the analysis saw 776 additional members adherent to RASAs, 1,651 additional members adherent to statins, and 414 additional members adherent to oral antidiabetics. Although medication expenditure increased for all 3 medication classes (RASAs: $274,963; statins: $730,083; oral antidiabetics: $100,529), medical costs decreased across all classes (RASAs: -$4,098,848; statins: -$5,549,699; oral antidiabetics: -$917,968). Total net health care costs decreased by $3,823,885 for RASAs (-$3.19 per member per month [PMPM]), $4,819,616 for statins (-$4.02 PMPM), and $817,438 for oral antidiabetics (-$0.68 PMPM). The entire Medicare Advantage population scenario analysis saw reductions in total health care costs of $1,081,394,737 for RASAs, $1,362,987,376 for statins, and $231,171,496 for oral antidiabetics. Dispensing chronic medications with blister-packaging for Medicare Advantage health plan patients was modeled to reduce HCRU and health care costs. Future studies are needed to assess whether the impact of blister-packaging medications is tied to reductions in HCRU and health care costs in real-world settings.

Applying Machine Learning Techniques: Uncertainty Quantification in Nonlinear Dynamics Characters Predictions via Gated Recurrent Unit-Based Reduced-Order Models

The development of reduced-order models has been a pivotal advancement in the computational analysis of fluid dynamics, substantially simplifying the complexity and boosting the efficiency of simulations. The accuracy and practicality of these models largely depend on the reduction techniques applied and the inherent characteristics of the fluid dynamics systems they represent. In this paper, we introduce an innovative machine-learning framework for assessing model uncertainty in computationally intensive reduced-order models. By combining subspace construction methods with advanced Bayesian inference techniques, our approach effectively captures the posterior distribution of model parameters, thereby providing an accurate representation of uncertainty in aerodynamic performance predictions. We employ the NACA0012 airfoil as a case study to validate our method’s ability to enhance the efficiency of reduced-order models and precisely measure the uncertainty inherent in predictions made by recurrent neural networks. It is important to note that our approach is influenced by specific constraints and variables that significantly impact the mean and variability of the predicted final lift coefficient distribution. Our findings indicate that setting the goodness of fit (R2) threshold above 0.985 markedly improves the correlation between Computational Fluid Dynamics (CFD) outcomes and model predictions, increasing from 72.2% to 97.9% as the interval amplification factor adjusts from 1.5 to 3. However, this adjustment causes a considerable expansion of the confidence interval, from 0.0737 to 0.1282, an increase of over 70%. Despite these challenges, our machine learning-based methodology provides essential insights into the further development of reduced-order modeling and uncertainty quantification in fluid dynamics. This highlights the need for ongoing research into the model parameters, especially in applications concerning aircraft control systems, to meet design specifications and ensure system reliability.

Robust clustering-based hybrid technique enabling reliable reservoir water quality prediction with uncertainty quantification and spatial analysis

Machine learning methodology has recently been considered a smart and reliable way to monitor water quality parameters in aquatic environments like reservoirs and lakes. This study employs both individual and hybrid-based techniques to boost the accuracy of dissolved oxygen (DO) and chlorophyll-a (Chl-a) predictions in the Wadi Dayqah Dam located in Oman. At first, an AAQ-RINKO device (CTD+ sensor) was used to collect water quality parameters from different locations and varying depths in the reservoir. Second, the dataset is segmented into homogeneous clusters based on DO and Chl-a parameters by leveraging an optimized K-means algorithm, facilitating precise estimations. Third, ten sophisticated variational-individual data-driven models, namely generalized regression neural network (GRNN), random forest (RF), gaussian process regression (GPR), decision tree (DT), least-squares boosting (LSB), bayesian ridge (BR), support vector regression (SVR), K-nearest neighbors (KNN), multilayer perceptron (MLP), and group method of data handling (GMDH) are employed to estimate DO and Chl-a concentrations. Fourth, to improve prediction accuracy, bayesian model averaging (BMA), entropy weighted (EW), and a new enhanced clustering-based hybrid technique called Entropy-ORNESS are employed to combine model outputs. The Entropy-ORNESS method incorporates a Genetic Algorithm (GA) to determine optimal weights and then combine them with EW weights. Finally, the inclusion of bootstrapping techniques introduces a stochastic assessment of model uncertainty, resulting in a robust estimator model. In the validation phase, the Entropy-ORNESS technique outperforms the independent models among the three fusion-based methods, yielding R2 values of 0.92 and 0.89 for DO and Chl-a clusters, respectively. The proposed hybrid-based methodology demonstrates reduced uncertainty compared to single data-driven models and two combination frameworks, with uncertainty levels of 0.24% and 1.16% for cluster 1 of DO and cluster 2 of Chl-a parameters. As a highlight point, the spatial analysis of DO and Chl-a concentrations exhibit similar pattern variations across varying depths of the dam according to the comparison of field measurements with the best hybrid technique, in which DO concentration values notably decrease during warmer seasons. These findings collectively underscore the potential of the upgraded weighted-based hybrid approach to provide more accurate estimations of DO and Chl-a concentrations in dynamic aquatic environments.

Uncertainties of nuclear level density estimated using Bayesian neural networks* *Supported by the the National Natural Science Foundation of China (12275359, 12375129, 11875323, 11961141003), the National Key R&D Program of China (2023YFA1606402), the Continuous Basic Scientific Research Project, the Funding of China Institute of Atomic Energy (YZ222407001301, YZ232604001601), and the Leading Innovation Project of the CNNC (LC192209000701, LC202309000201)

Nuclear level density (NLD) is a critical parameter for understanding nuclear reactions and the structure of atomic nuclei; however, accurate estimation of NLD is challenging owing to limitations inherent in both experimental measurements and theoretical models. This paper presents a sophisticated approach using Bayesian neural networks (BNNs) to analyze NLD across a wide range of models. It uniquely incorporates the assessment of model uncertainties. The application of BNNs demonstrates remarkable success in accurately predicting NLD values when compared to recent experimental data, confirming the effectiveness of our methodology. The reliability and predictive power of the BNN approach not only validates its current application but also encourages its integration into future analyses of nuclear reaction cross sections.

Pneumococcal Vaccination Strategies in 50-Year-Olds to Decrease Racial Disparities: A US Societal Perspective Cost-Effectiveness Analysis

ObjectivesThis study assesses the impact of expanding pneumococcal vaccination to all 50-year-olds to decrease racial disparities by estimating from the societal perspective, the cost-effectiveness of 20-valent pneumococcal conjugate vaccine (PCV20) and 15-valent conjugate vaccine followed by 23-valent polysaccharide vaccine (PCV15/PPSV23) for 50-year-olds. MethodsA Markov model compared the cost-effectiveness of PCV20 or PCV15/PPSV23 in all general population 50- and 65-years-olds compared with current US recommendations and with no vaccination in US Black and non-Black cohorts. US data informed model parameters. Pneumococcal disease societal costs were estimated using direct and indirect costs of acute illness and of pneumococcal-related long-term disability and mortality. Hypothetical 50-year-old cohorts were followed over their lifetimes with costs and effectiveness discounted 3% per year. Deterministic and probabilistic sensitivity analyses assessed model uncertainty. ResultsIn Black cohorts, PCV20 for all at ages 50 and 65 was the least costly strategy and had greater effectiveness than no vaccination and current recommendation strategies, whereas PCV15/PPSV23 at 50 and 65 cost more than $1 million per quality-adjusted life year (QALY) gained compared with PCV20 at 50 and 65. In non-Black cohorts, PCV20 at 50 and 65 cost $62 083/QALY and PCV15/PPSV23 at 50 and 65 cost more than $1 million/QALY with current recommendations, again being more costly and less effective. In probabilistic sensitivity analyses, PCV20 at 50 and 65 was favored in 85.7% (Black) and 61.8% (non-Black) of model iterations at a $100 000/QALY gained willingness-to-pay threshold. ConclusionsWhen considering the societal costs of pneumococcal disease, PCV20 at ages 50 and 65 years in the general US population is a potentially economically viable strategy, particularly in Black cohorts.

Microsimulation model of the cost-effectiveness of anifrolumab compared to belimumab in the United Arab Emirates

Introduction SLE imposes a significant morbidity and mortality as well as a substantial burden on the healthcare system. The model aimed to measure the cost-effectiveness of anifrolumab implementation against belimumab as an add-on-therapy to the standard of care (SoC) over a lifetime horizon for Emirati patients. Methodology A microsimulation model was used to assess the cost-effectiveness of anifrolumab against belimumab (IV/SC) as an add-on therapy to SoC in a hypothetical cohort of adult Emirati patients with systemic lupus erythematosus (SLE) over a lifetime horizon. The clinical data was captured from published clinical trials as; TULIP-1, TULIP-2, BLISS-52, BLISS-76 and BLISS‐SC. Health utility scores were constructed according to a linear regression model from the pooled data of the two TULIP Phase III trials of anifrolumab. Our model captures direct SLE-related medical costs from the Dubai Health Authority. Sensitivity analyses were conducted to assess model uncertainty. Results Using BICLA as a response criterion in the Johns Hopkins cohort, anifrolumab was found to be more effective than belimumab (IV/SC; the incremental discounted QALY of anifrolumab against belimumab was 0.42). The incremental cost-effectiveness ratio (ICER) of anifrolumab against belimumab IV and belimumab SC were AED 466,371 ($209,135) and AED 252,612 ($113,279), respectively, these ICERs are below the cost-effectiveness threshold in the United Arab Emirates (UAE) (three times gross domestic product capita; AED 592,278). In the Toronto lupus cohort, the ICER of anifrolumab against belimumab IV and belimumab SC were AED 491,403 ($220,360) and AED 276,642 ($124,055), respectively (anifrolumab was a cost-effective option vs. belimumab IV and belimumab SC). Conclusion The addition of anifrolumab to SoC is a cost-effective option versus belimumab for the treatment of adult patients with active, autoantibody-positive SLE, despite being allocated to SoC. Cost-effectiveness was demonstrated by a reduction in complications and organ damage, which reflected costs and outcomes.

Cost-effectiveness and budget impact analysis of Daratumumab, Lenalidomide and dexamethasone for relapsed-refractory multiple myeloma

BackgroundThe prominent efficacy in terms of increasing progression-free survival (PFS) of Daratumumab, Lenalidomide and dexamethasone (DRd) triplet therapy versus Carfilzomib, Lenalidomide and dexamethasone (KRd) was proven previously in relapsed-refractory multiple myeloma (RRMM). However, the cost effectiveness of DRd versus KRd is unknown.MethodsWe developed a Markov model by using an Iranian payer perspective and a 10-year time horizon to estimate the healthcare cost, Quality-adjusted life years (QALYs) and life years gain (LYG) for DRd and KRd triplet therapies. Clinical data were obtained from meta-analyses and randomized clinical trials (RCTs). One-way and probabilistic sensitivity analysis were performed to assess model uncertainty. Budget impact analysis of 5 years of treatment under the DRd triplet therapy was also analysed.ResultsDRd was estimated to be more effective compared to KRd, providing 0.28 QALY gain over the modelled horizon. DRd-treated patients incurred $264 in total additional costs. The incremental cost utility ratio (ICUR) and cost effectiveness ratio (ICER) were $956/QALY and $472/LYG respectively.The budget impact analysis indicates that adding Daratumumab to Lenalidomide and dexamethasone regimen, in the first 5 years, will increase the healthcare system’s expenses by $6.170.582.ConclusionDRd triplet therapy compared to KRd is a cost-effective regimen for RRMM under Iran willingness-to-pay threshold.

The Lagrangian Atmospheric Radionuclide Transport Model (ARTM) – sensitivity studies and evaluation using airborne measurements of power plant emissions

Abstract. The Atmospheric Radionuclide Transport Model (ARTM) operates at the meso-γ scale and simulates the dispersion of radionuclides originating from nuclear facilities under routine operation within the planetary boundary layer. This study presents the extension and validation of this Lagrangian particle dispersion model and consists of three parts: (i) a sensitivity study that aims to assess the impact of key input parameters on the simulation results, (ii) the evaluation of the mixing properties of five different turbulence models using the well-mixed criterion, and (iii) a comparison of model results to airborne observations of carbon dioxide (CO2) emissions from a power plant and the evaluation of related uncertainties. In the sensitivity study, we analyse the effects of the stability class, roughness length, zero-plane displacement factor, and source height on the three-dimensional plume extent as well as the distance between the source and maximum concentration at the ground. The results show that the stability class is the most sensitive input parameter as expected. The five turbulence models are the default turbulence models of ARTM 2.8.0 and ARTM 3.0.0, one alternative built-in turbulence model of ARTM, and two further turbulence models implemented for this study. The well-mixed condition tests showed that all five turbulence models are able to preserve an initially well-mixed atmospheric boundary layer reasonably well. The models deviate only 6 % from the expected uniform concentration below 80 % of the mixing layer height, except for the default turbulence model of ARTM 3.0.0 with deviations of up to 18 %. CO2 observations along a flight path in the vicinity of the lignite power plant Bełchatów, Poland, measured by the Deutsches Zentrum für Luft- und Raumfahrt (DLR) Cessna aircraft during the Carbon Dioxide and Methane Mission (CoMet) campaign in 2018 allowed for evaluation of model performance for the different turbulence models under unstable boundary layer conditions. All simulated mixing ratios are of the same order of magnitude as the airborne in situ data. An extensive uncertainty analysis using probability distribution functions, statistical tests, and direct spatio-temporal comparisons of measurements and model results help to quantify the model uncertainties. With the default turbulence setups of ARTM versions 2.8.0 and 3.0.0, the plume widths are underestimated by up to 50 %, resulting in a strong overestimation of the maximum plume CO2 mixing ratios. The comparison of the three alternative turbulence models shows good agreement of the peak plume CO2 concentrations, the CO2 distribution within the plumes, and the plume width, with a 30 % deviation in the peak CO2 concentration and a less than 25 % deviation in the measured CO2 plume width. Uncertainties in the simulations may arise from the different spatial and temporal resolutions of simulations and measurements in addition to the turbulence parametrisation and boundary conditions. The results of this work may help to improve the accurate representation of real plumes in very unstable atmospheric conditions through the selection of distinct turbulence models. Further comparisons at different stability regimes are required for a final assessment of model uncertainties.

Cost-effectiveness analysis of robot-assisted laparoscopic surgery for complex pediatric surgical conditions.

Robotics has been used safely and successfully in a variety of adult surgeries and is gradually gaining ground in pediatrics. While the benefits of robotic-assisted surgery in disease treatment are well recognized, its high cost has led to questions. To investigate whether robotic-assisted laparoscopic surgery (RALS) is cost-effective compared to conventional laparoscopic surgery (LS) in pediatric surgery, we attempted to construct a model to perform an analysis of these two surgical approaches using Python statistical analysis software. We selected four common complex pediatric surgical conditions (choledochal cyst, Hirschsprung's disease, vesicoureteral reflux, and congenital hydronephrosis) from three systems (pediatric hepatobiliary, gastroenterology, and urology). Models were constructed using Python statistical software to compare hospital costs and surgical outcomes for RALS and LS. In addition, we performed a preferred strategy analysis for both surgical modalities while assessing model uncertainty using one-way sensitivity analysis. For the four diseases, the operative time decreased sequentially. The total inpatient costs of RALS were 10,816.72, 9145.44, 8414.29, 7973.58 dollars, respectively, yielding 1.789, 1.712, 1.749, 1.792 quality adjustment life years (QALYs) over two years post-operatively. The incremental cost of RALS relative to LS for each disease was 3523.44, 3200.20, 3049.79, 3043.66 dollars, respectively, with an incremental utility of 0.060, 0.054, 0.051, 0.050 QALYs. The incremental cost-effectiveness ratios (ICERs) for RALS for each of the four diseases were 58,724.01, 59,262.95, 59,799.79, 60,873.20 dollars/QALY, all less than 100,000 dollars/QALY. The cost of robot consumables was the main incremental cost of RALS and had the most significant impact on the model. For the four pediatric surgical conditions described above, RALS has higher inpatient costs than LS, but it has better postoperative outcomes, and all four RALS treatments are cost-effective. Children with complex diseases and long operative times appear to benefit more from RALS.

Uncertainty assessment for three-dimensional hydrodynamic and fecal coliform modeling in the Danshuei River estuarine system: The influence of first-order parametric decay reaction

Modeling fecal contamination in water bodies is of importance for microbiological risk assessment and management. This study investigated the transport of fecal coliform (e.g., up to 2.1 × 106 CFU/100 ml at the Zhongshan Bridge due to the main point source from the Xinhai Bridge) in the Danshuei River estuarine system, Taiwan with the main focus on assessing model uncertainty due to three relevant parameters for the microbial decay process. First, a 3D hydrodynamic-fecal coliform model (i.e., SCHISM-FC) was developed and rigorously validated against the available data of water level, velocity, salinity, suspended sediment and fecal coliform measured in 2019. Subsequently, the variation ranges of decay reaction parameters were considered from several previous studies and properly determined using the Monte Carlo simulations. Our analysis showed that the constant ratio of solar radiation (α) as well as the settling velocity (vs) had the normally-distributed variations while the attachment fraction of fecal coliform bacteria (Fp) was best fitted by the Weibull distribution. The modeled fecal coliform concentrations near the upstream (or downstream) stations were less sensitive to those parameter variations (see the smallest width of confidence interval about 1660 CFU/100 ml at the Zhongzheng Bridge station) due to the dominant effects of inflow discharge (or tides). On the other hand, for the middle parts of Danshuei River where complicated hydrodynamic circulation and decay reaction occurred, the variations of parameters led to much larger uncertainty in modeled fecal coliform concentration (see a wider confidence interval about 117,000 CFU/100 ml at the Bailing Bridge station). Overall, more detailed information revealed in this study would be helpful while the environmental authority needs to develop a proper strategy for water quality assessment and management. Owing to the uncertain decay parameters, for instance, the modeled fecal coliform impacts at Bailing Bridge over the study period showed a 25 % difference between the lowest and highest concentrations at several moments. For the detection of pollution occurrence, the highest to lowest probabilities for a required fecal coliform concentration (e.g., 260,000 CFU/100 ml over the environmental regulation) at Bailing Bridge was possibly greater than three.

Modeling of 3D geometry uncertainty in Scan-to-BIM automatic indoor reconstruction

The rapidly expanding field of Scan-to-BIM applications highlights the importance of model uncertainty assessment in describing the quality of modeling results. Although there have been recent research advancements in point cloud-based building modeling, there has been limited investigation into accurately analyzing error propagation. This paper estimates the geometry uncertainty in 3D modeling based on a strict application of geodetic stochastic modeling. Statistical uncertainty is incorporated into the building reconstruction process and procedures that enable self-verification within this process are developed. The method can be successfully used to evaluate the dimensional uncertainty of generated BIMs, which is especially important in the field of civil engineering with high accuracy requirements concerning metric quality control. Follow-up research will also consider systematic errors and apply the methods to other 3D point cloud acquisition techniques.

Full‐Waveform Tomography of the African Plate Using Dynamic Mini‐Batches

AbstractWe present a seismic model of the Africa Plate, constructed with the technique of full‐waveform inversion. The purpose of our model is to serve as a foundation for quantitative geodynamic and geochemical interpretation, earthquake‐induced ground motion predictions, and earthquake source inversion. Starting from the first‐generation Collaborative Seismic Earth Model, we invert seismograms filtered to a minimum period of 35 s and compute gradients of the misfit function with respect to the model parameters using the adjoint state method. We use dynamically changing mini‐batches of the complete data set to compute approximate gradients at each iteration. This approach has three significant advantages: (a) it reduces computational costs for model updates and the inversion, (b) it enables the use of larger datasets without increasing iteration costs, and (c) it makes it trivial to assimilate new data since we can extend the data set without changing the misfit function. We invert data from 397 unique earthquakes and 184,356 unique source‐receiver pairs. We clearly image tectonic features such as the Afar triple junction and low‐velocity zones below the Hoggar, Aïr, and Tibesti Mountains, pronounced more than in earlier works. Finally, we introduce a new strategy to assess model uncertainty. We deliberately perturb the final model, perform additional mini‐batch iterations, and compare the result with the original final model. This test uses actual seismic data instead of artificially generated synthetic data and requires no assumptions about the linearity of the inverse problem.

Assessing the difference between integrated quantiles and integrated cumulative distribution functions

This paper offers a mathematical invention that shows how to convert integrated quantiles, which often appear in risk measures, into integrated cumulative distribution functions, which are technically more tractable from various perspectives. The invention helps to avoid a number of technical assumptions that have been traditionally imposed when working with quantities containing quantiles. In particular it helps to completely avoid the requirement of the existence of a probability density function. The developed results explain and illustrate the invention, whose byproducts include the assessment of model uncertainty and misspecification, and the derivation of statistical inference results.

Validation of computational fluid dynamics simulations for determining pressure loss coefficients of ventilation components

Ventilation systems include a variety of components for which necessary pressure loss data is often unavailable. Computational fluid dynamics simulations could substitute for expensive measurements, but validation simulations with suitable data are crucial to assess model uncertainties. Existing CFD validation studies either did not focus specifically on pressure losses, only covered few components, or did not include recent developments in turbulence modelling. In the present work, 33 bends, 4 gates and 2 tees were simulated using a consistent approach. Computational fluid dynamics simulations were validated with published data: rectangular high-edge and wide-edge bends from the experimental dataset of Sprenger, gates and diverging tees from the SMACMA guide. The considered flows cover important basic flow phenomena: deflection, splitting and flow separation. The 39 components were simulated with three turbulence models at 14 Reynolds numbers. The simulations predicted pressure loss coefficients accurately for various components. Cases with strong flow separation regions were most challenging. The model prediction uncertainty was assessed by carrying out simulations with three selected turbulence models. As in the experimental data from Sprenger, the simulations showed a distinct dependence of pressure loss coefficients on the Reynolds number for bends. In contrast, for abrupt deflections and flow separation at sharp edges, the Reynolds number dependency was minor. Technical pressure loss data of ductwork components is needed for the dimensioning, optimisation, and energy assessment of ventilation systems. The present validation study assesses the present state of the art of CFD simulations to determine pressure loss coefficients and the resulting prediction uncertainties.

Model Uncertainty Assessment for Symmetric and Right-Skewed Distributions

Model Uncertainty Assessment for Symmetric and Right-Skewed Distributions

A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches

With the significant increase in WQI applications worldwide and lack of specific application guidelines, accuracy and reliability of WQI models is a major issue. It has been reported that WQI models produce significant uncertainties during the various stages of their application including: (i) water quality indicator selection, (ii) sub-index (SI) calculation, (iii) water quality indicator weighting and (iv) aggregation of sub-indices to calculate the overall index. This research provides a robust statistically sound methodology for assessment of WQI model uncertainties. Eight WQI models are considered. The Monte Carlo simulation (MCS) technique was applied to estimate model uncertainty, while the Gaussian Process Regression (GPR) algorithm was utilised to predict uncertainties in the WQI models at each sampling site. The sub-index functions were found to contribute to considerable uncertainty and hence affect the model reliability – they contributed 12.86% and 10.27% of uncertainty for summer and winter applications, respectively. Therefore, the selection of sub-index function needs to be made with care. A low uncertainty of less than 1% was produced by the water quality indicator selection and weighting processes. Significant statistical differences were found between various aggregation functions. The weighted quadratic mean (WQM) function was found to provide a plausible assessment of water quality of coastal waters at reduced uncertainty levels. The findings of this study also suggest that the unweighted root means squared (RMS) aggregation function could be potentially also used for assessment of coastal water quality. Findings from this research could inform a range of stakeholders including decision-makers, researchers, and agencies responsible for water quality monitoring, assessment and management.