Model Output Variables Research Articles

A Bayesian inference approach to account for multiple sources of uncertainty in a macroalgae based integrated multi-trophic aquaculture model

A Bayesian inference method was employed to quantify uncertainty in an Integrated Multi-Trophic Aquaculture (IMTA) model. A deterministic model was reformulated as a Bayesian Hierarchical Model (BHM) with uncertainty in the parameters accounted for using “prior” distributions and unresolved time varying processes modelled using auto-regressive processes. Observations of kelp grown in 3 seeding densities around salmon pens were assimilated using a Sequential Monte Carlo method implemented within the LibBi package. This resulted in a considerable reduction in the variability in model output for both the observed and unobserved state variables. A reduction in variance between the prior and posterior was observed for a subset of model parameters which varied with seeding density. Kullback–Liebler (KL) divergence method showed the reduction in variability of the state and parameters was approximately 90%. A low to medium seeding density results in the most efficient removal of excess nutrients in this simple system.

Assessing the uncertainty of glacier mass-balance simulations in the European Arctic based on variance decomposition

Abstract. State-of-the-art numerical snowpack models essentially rely on observational data for initialization, forcing, parametrization, and validation. Such data are available in increasing amounts, but the propagation of related uncertainties in simulation results has received rather limited attention so far. Depending on their complexity, even small errors can have a profound effect on simulations, which dilutes our confidence in the results. This paper aims at quantification of the overall and fractional contributions of some archetypical measurement uncertainties on snowpack simulations in arctic environments. The sensitivity pattern is studied at two sites representing the accumulation and ablation area of the Kongsvegen glacier (Svalbard), using the snowpack scheme Crocus. The contribution of measurement errors on model output variance, either alone or by interaction, is decomposed using global sensitivity analysis. This allows one to investigate the temporal evolution of the fractional contribution of different factors on key model output metrics, which provides a more detailed understanding of the model's sensitivity pattern. The analysis demonstrates that the specified uncertainties in precipitation and long-wave radiation forcings had a strong influence on the calculated surface-height changes and surface-energy balance components. The model output sensitivity patterns also revealed some characteristic seasonal imprints. For example, uncertainties in long-wave radiation affect the calculated surface-energy balance throughout the year at both study sites, while precipitation exerted the most influence during the winter and at the upper site. Such findings are valuable for identifying critical parameters and improving their measurement; correspondingly, updated simulations may shed new light on the confidence of results from snow or glacier mass- and energy-balance models. This is relevant for many applications, for example in the fields of avalanche and hydrological forecasting.

Integrating Genomics with Nutrition Models to Improve the Prediction of Cattle Performance and Carcass Composition under Feedlot Conditions.

Cattle body composition is difficult to model because several factors affect the composition of the average daily gain (ADG) of growing animals. The objective of this study was to identify commercial single nucleotide polymorphism (SNP) panels that could improve the predictability of days on feed (DOF) to reach a target United States Department of Agriculture (USDA) grade given animal, diet, and environmental information under feedyard conditions. The data for this study was comprised of crossbred heifers (n = 681) and steers (n = 836) from commercial feedyards. Eleven molecular breeding value (MBV) scores derived from SNP panels of candidate gene polymorphisms and two-leptin gene SNP (UASMS2 and E2FB) were evaluated. The empty body fat (EBF) and the shrunk body weight (SBW) at 28% EBF (AFSBW) were computed by the Cattle Value Discovery System (CVDS) model using hip height (EBFHH and AFSBWHH) or carcass traits (EBFCT and AFSBWCT) of the animals. The DOFHH was calculated when AFSBWHH and ADGHH were used and DOFCT was calculated when AFSBWCT and ADGCT were used. The CVDS estimates dry matter required (DMR) by individuals fed in groups when observed ADG and AFSBW are provided. The AFSBWCT was assumed more accurate than the AFSBWHH because it was computed using carcass traits. The difference between AFSBWCT and AFSBWHH, DOFCT and DOFHH, and DMR and dry matter intake (DMI) were regressed on the MBV scores and leptin gene SNP to explain the variation. Our results indicate quite a large range of correlations among MBV scores and model input and output variables, but MBV ribeye area was the most strongly correlated with the differences in DOF, AFSBW, and DMI by explaining 8, 13.2 and 6.5%, respectively, of the variation. This suggests that specific MBV scores might explain additional variation of input and output variables used by nutritional models in predicting individual animal performance.

Discrete non-parametric kernel estimation for global sensitivity analysis

This work investigates the discrete kernel approach for evaluating the contribution of the variance of discrete input variables to the variance of model output, via analysis of variance (ANOVA) decomposition. Until recently only the continuous kernel approach has been applied as a metamodeling approach within sensitivity analysis framework, for both discrete and continuous input variables. Now the discrete kernel estimation is known to be suitable for smoothing discrete functions. We present a discrete non-parametric kernel estimator of ANOVA decomposition of a given model. An estimator of sensitivity indices is also presented with its asymtotic convergence rate. Some simulations on a test function analysis and a real case study from agricultural have shown that the discrete kernel approach outperforms the continuous kernel one for evaluating the contribution of moderate or most influential discrete parameters to the model output.

Algebraic equilibrium solution of tissue nitrogen quota in algae and the discrepancy between calibrated parameters and physiological properties

Tissue nutrient concentrations are a key factor in determining primary production in a variety of algae, for example the marine macroalga Ulva. We present a novel algebraic solution to calculate the equilibrium tissue nitrogen concentration or “quota” Q. The solution is derived from a classical mechanistic description of “luxury uptake” in marine macroalgae using a computer algebra system. Forced by ammonium (NH4+) and nitrate plus nitrite (NOx−) concentrations, water temperature and irradiance, equilibrium Q can be calculated directly without the need for numerical integration, and the model performs well in reproducing observations of Q in frondose Ulva spp. A Sobol’ global sensitivity analysis reveals that the degree of uncertainty in physiological parameters has a similar magnitude of influence on model output as the typical environmental range of nutrient forcing data. The environmental forcing variables NH4+ and NOx− together account for 60% of variance in model output, while the two most influential physiological parameters together account for another 32% of variance. Repeated parameter calibrations with random first guesses and evolutionary adaptations lead to broad and even multimodal distributions for some parameters, as well as values at the extremes of their literature ranges. This shows that although model performance as quantified by statistical measures is high, individual calibrations are not sufficient to give reliable parameter estimates that can be interpreted as physiological system properties.

Sensitivity and Interaction Analysis Based on Sobol’ Method and Its Application in a Distributed Flood Forecasting Model

Sensitivity analysis is a fundamental approach to identify the most significant and sensitive parameters, helping us to understand complex hydrological models, particularly for time-consuming distributed flood forecasting models based on complicated theory with numerous parameters. Based on Sobol’ method, this study compared the sensitivity and interactions of distributed flood forecasting model parameters with and without accounting for correlation. Four objective functions: (1) Nash–Sutcliffe efficiency (ENS); (2) water balance coefficient (WB); (3) peak discharge efficiency (EP); and (4) time to peak efficiency (ETP) were implemented to the Liuxihe model with hourly rainfall-runoff data collected in the Nanhua Creek catchment, Pearl River, China. Contrastive results for the sensitivity and interaction analysis were also illustrated among small, medium, and large flood magnitudes. Results demonstrated that the choice of objective functions had no effect on the sensitivity classification, while it had great influence on the sensitivity ranking for both uncorrelated and correlated cases. The Liuxihe model behaved and responded uniquely to various flood conditions. The results also indicated that the pairwise parameters interactions revealed a non-ignorable contribution to the model output variance. Parameters with high first or total order sensitivity indices presented a corresponding high second order sensitivity indices and correlation coefficients with other parameters. Without considering parameter correlations, the variance contributions of highly sensitive parameters might be underestimated and those of normally sensitive parameters might be overestimated. This research laid a basic foundation to improve the understanding of complex model behavior.

An Assessment of Numerical Weather Prediction–Derived Low-Cloud-Base Height Forecasts

Abstract As demand for flight operations in Antarctica grows, accurate weather forecasting of cloud properties such as extent, cloud base, and cloud-top altitude becomes essential. The primary aims of this work are to ascertain relationships between numerical weather prediction (NWP) model output variables and surface-observed cloud properties and to develop low-cloud-base (<2000 m) height prediction algorithms for use across Antarctica to assist in low-cloud forecasting for aircraft operations. NWP output and radiosonde data are assessed against surface observations, and the relationship between the relative humidity RH profile and the height of the observed low-cloud base is investigated. The ability of NWP-derived RH and ice–water cloud optical depth profiles to represent the observed low-cloud conditions around each of the three Australian stations in East Antarctica is assessed. NWP-derived RH is drier than that reported by radiosonde from ground level up to ~2000 m. This trend reverses in the higher troposphere, and the largest positive difference is observed at ~10 000 m. A consequence is very low RH thresholds are needed for low-cloud-base height prediction using NWP RH profiles. RH and optical depth–based threshold techniques all show skill in reproducing the observed cloud-base height at all Australian Antarctic stations, but the radiosonde-derived RH technique is superior in all cases. This comparison of three low-cloud-base height retrieval techniques provides the first documented assessment of the relative efficacy of each technique in Antarctica.

A new framework of variance based global sensitivity analysis for models with correlated inputs

In the past few decades, variance based global sensitivity analysis for models with only uncorrelated inputs has been well developed. It aims at investigating the impact of variations in uncorrelated inputs on the variation of a model output and ranking the importance of the inputs. However, for models with correlated inputs, only a few researches have been done and the existing theory of variance based global sensitivity is not so consummate. In this article, a new framework of variance based global sensitivity analysis is presented, which is suitable for models with both uncorrelated and correlated inputs. With this new framework, the variance based global sensitivity analysis for models with correlated variables can be conducted conveniently and the variance contributions of a correlated variable to the variance of model output can be identified and interpreted distinctly.

A model for the dynamics of the free-living stages of equine cyathostomins

Anthelmintic resistance in strongyle nematode parasites of horses is an expanding global problem and steps need to be taken to slow its development before control becomes more problematic. A move away from traditional deworming programmes, involving frequent whole-herd treatments with broad spectrum anthelmintics, to a more strategic or targeted use of chemicals is required. However, anthelmintic resistance management strategies which also maintain effective control are invariably more complicated and often require a greater understanding of both nematode epidemiology and grazing management, than does the simple routine use of chemicals. Here, as a first step in applying a modelling approach to resistance management in horses, a model is proposed to describe the dynamics on pasture of the free-living stages of equine cyathostomins. Firstly, the development and survival of the pre-infective stages is considered as a single process driven by temperature, and secondly, two populations of infective stage larvae (L3) are considered; those within the faecal pat and those on the herbage. Both are modelled using the box-car train approach which allows for variable development rates within a cohort of individuals and full overlap of generations. Uniquely, L3 survival is modelled as an ageing process where larvae progress through physiological age classes at a rate determined by temperature and rainfall. Model output reflects the dynamics of free-living stages under a range of environments. Under extreme cold, there is no development to L3 but eggs can survive for long periods to develop if conditions become favourable, while L3 survival is reduced under repeated freeze–thaw cycles. Under tropical conditions, development is rapid and a large number of L3 can be produced but survival of L3 is short. In temperate climates development tends to be slower, with large numbers of L3 produced over the warmer months but fewer over winter, and L3 survival tends to be higher all year round. Although attempts to validate model output against field studies were compromised by the lack of published detail or an inability to access specific weather records, outputs averaged over multiple sets of weather data was often appropriate for that location. Variation in model output when using weather data sets which started on different days within the same week suggests that day-to-day differences in weather may affect the number of L3 developing on pasture and that optimisation of anthelmintic use to minimise pasture infectivity may require a more detailed understanding of weather effects than previously thought.

A sensitivity and probability analysis of the safety of deep geological repositories situated in crystalline rock

The aim of this paper is to present the sensitivity and probability analysis of a model of the Czech safety concept of deep geological waste repository. The sensitivity analysis was performed for a set of critical radionuclides, 14C, 36Cl, 79Se, 129I and 226Ra using a variance based method. The main contributors to the variance of model output were identified. It was shown that, if the model output is properly transformed before performing sensitivity analysis, then the calculated sensitivity coefficients are robust indicators of the importance of uncertainties of the individual parameters.

An Objective Analysis Method for Perceptual Quality of a Virtual Bass System

Due to the physical size and frequency response constraints of miniaturized and flat panel loudspeakers, low frequency reproduction from these loudspeakers is generally limited and unsatisfactory. The virtual bass system (VBS) enhances the bass performance of such loudspeakers by tricking the human brain to perceive the fundamental frequency from its higher harmonics. Problematically, the additional harmonics from VBS can also introduce perceptual distortion and reduce the audio quality. Therefore, a reliable method to assess the quality of VBS-enhanced signals is necessary in the designing of VBS. Since subjective experiments are often time-consuming and may be inconsistent, it is desirable to develop an objective assessment method for VBS. Earlier studies only utilized some simple objective metrics, which generally do not consider the human auditory model and are unable to accurately predict the perceptual quality of VBS. In this paper, we introduce a perceptual quality-assessment method for VBS based on the model output variables (MOVs) of the ITU Recommendation ITU-R BS.1387. Suitable combinations of MOVs are selected to derive perceptual quality metrics that correlate closely to the subjective quality. A verification experiment is presented to justify the accuracy of the metrics.

Identifying sensitive sources and key control handles for the reduction of greenhouse gas emissions from wastewater treatment

This research investigates the effects of adjusting control handle values on greenhouse gas emissions from wastewater treatment, and reveals critical control handles and sensitive emission sources for control through the combined use of local and global sensitivity analysis methods. The direction of change in emissions, effluent quality and operational cost resulting from variation of control handles individually is determined using one-factor-at-a-time sensitivity analysis, and corresponding trade-offs are identified. The contribution of each control handle to variance in model outputs, taking into account the effects of interactions, is then explored using a variance-based sensitivity analysis method, i.e., Sobol's method, and significant second order interactions are discovered. This knowledge will assist future control strategy development and aid an efficient design and optimisation process, as it provides a better understanding of the effects of control handles on key performance indicators and identifies those for which dynamic control has the greatest potential benefits. Sources with the greatest variance in emissions, and therefore the greatest need to monitor, are also identified. It is found that variance in total emissions is predominantly due to changes in direct N2O emissions and selection of suitable values for wastage flow rate and aeration intensity in the final activated sludge reactor is of key importance. To improve effluent quality, costs and/or emissions, it is necessary to consider the effects of adjusting multiple control handles simultaneously and determine the optimum trade-off.

English

Mobile ad-hoc network technology has gained popularity in recent years by researchers on account of its flexibility, low cost and eases of deployment. The objective of proposed R2M2 model is to evaluate the performance of MANET (Mobile ad hoc Network ) operating with DSR( Dynamic Source Routing) as routing protocol. The R2M2 model for MANET is simulated and implemented using network simulator ns 2.34 and validated using Bayesian rules. The R2M2 model is able to find out the probability of having certain behavior (able to decide reliable or not reliable) of routes, in uncertain observations (not able to decide whether the route is reliable or not) in presence of drop and delay. For performance evaluation of MANET using the proposed R2M2 model input variables (like node density, number of active connections, duration of communication, node movement speed, pause time & data transfer rate etc. ) and output variables drop rate and delay have been taken as output variables. The R2M2 model helps in deciding the certain and uncertain behavior of routes and consequently the nodes and is found satisfactory.

The Potential for Mesoscale Visibility Predictions with a Multimodel Ensemble

Abstract This work examines the viability of producing short-range (<20 h) probabilistic fog predictions in remote locations, absent an observational history, using an uncalibrated 4-km, 10-member Weather Research and Forecasting Model (WRF) ensemble configured to closely match the Air Force Weather Agency Mesoscale Ensemble Forecast Suite. Three distinct sources of error in the final predictions are considered separately to facilitate a better understanding of the total error and appropriate mitigation strategies. These include initial condition error, parameterization of subgrid-scale processes, and error in the visibility parameterization used to convert NWP model output variables to visibility. The raw WRF predictions are generally not skillful in valley and coastal regions, where they produce a shortage of light fog predictions with visibilities of 1–7 mi (1.6–11.3 km) in favor of excessive forecasts of zero cloud water, corresponding to no fog. Initial condition error and visibility parameterization error are shown to play a relatively minor role compared to error in the parameterization of subgrid-scale processes. This deficiency is caused by a negative relative humidity bias, which results from a warm overnight bias. A second-order source of error arises from an inconsistent delineation of fog and haze in the NWP model compared to the verifying observations. Results show that under most conditions it is necessary to deviate from the perfect-prog assumption, and to introduce some method of statistical postprocessing to obtain skillful visibility predictions from the ensemble.

A new multimedia contaminant fate model for China: How important are environmental parameters in influencing chemical persistence and long-range transport potential?

We present a new multimedia chemical fate model (SESAMe) which was developed to assess chemical fate and behaviour across China. We apply the model to quantify the influence of environmental parameters on chemical overall persistence (POV) and long-range transport potential (LRTP) in China, which has extreme diversity in environmental conditions. Sobol sensitivity analysis was used to identify the relative importance of input parameters. Physicochemical properties were identified as more influential than environmental parameters on model output. Interactive effects of environmental parameters on POV and LRTP occur mainly in combination with chemical properties. Hypothetical chemicals and emission data were used to model POV and LRTP for neutral and acidic chemicals with different KOW/DOW, vapour pressure and pKa under different precipitation, wind speed, temperature and soil organic carbon contents (fOC). Generally for POV, precipitation was more influential than the other environmental parameters, whilst temperature and wind speed did not contribute significantly to POV variation; for LRTP, wind speed was more influential than the other environmental parameters, whilst the effects of other environmental parameters relied on specific chemical properties. fOC had a slight effect on POV and LRTP, and higher fOC always increased POV and decreased LRTP. Example case studies were performed on real test chemicals using SESAMe to explore the spatial variability of model output and how environmental properties affect POV and LRTP. Dibenzofuran released to multiple media had higher POV in northwest of Xinjiang, part of Gansu, northeast of Inner Mongolia, Heilongjiang and Jilin. Benzo[a]pyrene released to the air had higher LRTP in south Xinjiang and west Inner Mongolia, whilst acenaphthene had higher LRTP in Tibet and west Inner Mongolia. TCS released into water had higher LRTP in Yellow River and Yangtze River catchments. The initial case studies demonstrated that SESAMe performed well on comparing POV and LRTP of chemicals in different regions across China in order to potentially identify the most sensitive regions. This model should not only be used to estimate POV and LRTP for screening and risk assessments of chemicals, but could potentially be used to help design chemical monitoring programmes across China in the future.

Using probabilistic sampling-based sensitivity analyses for indoor air quality modelling

We develop a probabilistic framework for modelling indoor air quality in housing stocks, selecting appropriate sensitivity analyses to understand indoor air quality determinants, and constructing a reliable metamodel from the most relevant determinants to allow quick assessments of future intervention scenarios.The replicated Latin Hypercube sampling method is shown to be efficient at propagating variations between model input and output variables. A comparison of a range of sample-based sensitivity methods shows that an initial visual assessment can help to select appropriate sensitivity analyses, as they test for different types of relations (i.e. linear, monotonic, and non-monotonic). An advantage of linear regression methods is that the total output can be apportioned to various input variables. The advantage of tests with correlation coefficients is that the associated p-values can be used to assess whether input variables are significant. An artificial neural network constructed from a reduced set of input variables selected at a 5% level of significance is able to accurately predict indoor air quality.In the application of the framework to the modelling of winter indoor air quality in single-storey flats in England, the drivers for internally- and externally-generated PM2.5 are found to be different, therefore allowing interventions that reduce both concentrations simultaneously. Principal determinants for externally-generated PM2.5 are the internal deposition rate of PM2.5, weather-corrected volumetric infiltration rate, and ambient concentration of PM2.5, while for PM2.5 produced by gas cooking, they are the kitchen window opening area, generation rate of PM2.5, and indoor temperature.

Avaliação do Modelo WRF para o Ano de 2009

In this present work the WRF model is tested over south Brazil. The assessment is done comparing model output variables to variables collected at INMET weather stations located in Curitiba-PR, Paranagua-PR, Sao Jose-SC, Idaial-SC, Passo Fundo-RS and Rio Grande-RS. Conclusions based on Root Mean Square Error show that the variable which has greater predictability is Sea Level Pressure, followed by Surface Temperature, Relative Humidity and Wind Vector at Surface. Bias calculations suggest that tendencies of the model must be investigated by running WRF with different parameterizations schemes. The Bias range for Sea Level Pressure, Surface Temperature, Relative Humidity and Surface Wind are respectively -6,5 e 1,7 hPa; -5,0 e 5.0 oC; -37.0 e 18.0 %; -13,0 e 12,0 ms-1.

Experimental Design for Dynamics Identification of Cellular Processes

We address the problem of using nonlinear models to design experiments to characterize the dynamics of cellular processes by using the approach of the Maximally Informative Next Experiment (MINE), which was introduced in W. Dong et al. (PLoS ONE 3(8):e3105, 2008) and independently in M.M. Donahue et al. (IET Syst. Biol. 4:249-262, 2010). In this approach, existing data is used to define a probability distribution on the parameters; the next measurement point is the one that yields the largest model output variance with this distribution. Building upon this approach, we introduce the Expected Dynamics Estimator (EDE), which is the expected value using this distribution of the output as a function of time. We prove the consistency of this estimator (uniform convergence to true dynamics) even when the chosen experiments cluster in a finite set of points. We extend this proof of consistency to various practical assumptions on noisy data and moderate levels of model mismatch. Through the derivation and proof, we develop a relaxed version of MINE that is more computationally tractable and robust than the original formulation. The results are illustrated with numerical examples on two nonlinear ordinary differential equation models of biomolecular and cellular processes.

Modeling layer thickness of duplex ceramic (chromium carbonitride) coating on cold work tool steel using fuzzy logic

The paper presents some results of the research connected with the development of new approach based on the fuzzy logic FL of predicting the layer thickness of the chromium carbonitride CrC,N in 1.2210 steel after thermoreactive deposition and diffusion TRD coating. The independent variables in the model are nitriding time, salt bath compositions ratio, salt bath aging time, ferro-chromium particle size, ferro-chromium weight percent, salt bath temperature and coating time. The model output variable was the layer thickness of coating. For purpose of constructing these models, 105 different experimental data were gathered from the experiment. The data used in the fuzzy logic model is arranged in a format of eigth parameters that cover the input and output parameter. In this model, the training and testing results in the fuzzy logic systems have shown strong potential for prediction of effects of pre-nitriding, salt bath condition and coating variables on layer thickness of CrC,N coating on 1.2210 steel.

Risk of Norovirus Gastroenteritis from Consumption of Vegetables Irrigated with Highly Treated Municipal Wastewater—Evaluation of Methods to Estimate Sewage Quality

Quantitative microbial risk assessment was used to assess the risk of norovirus gastroenteritis associated with consumption of raw vegetables irrigated with highly treated municipal wastewater, using Melbourne, Australia as an example. In the absence of local norovirus concentrations, three methods were developed: (1) published concentrations of norovirus in raw sewage, (2) an epidemiological method using Melbourne prevalence of norovirus, and (3) an adjustment of method 1 to account for prevalence of norovirus. The methods produced highly variable results with estimates of norovirus concentrations in raw sewage ranging from 10(4) per milliliter to 10(7) per milliliter and treated effluent from 1 × 10(-3) per milliliter to 3 per milliliter (95th percentiles). Annual disease burden was very low using method 1, from 4 to 5 log10 disability adjusted life years (DALYs) below the 10(-6) threshold (0.005-0.1 illnesses per year). Results of method 2 were higher, with some scenarios exceeding the threshold by up to 2 log10 DALYs (up to 95,000 illnesses per year). Method 3, thought to be most representative of Melbourne conditions, predicted annual disease burdens >2 log10 DALYs lower than the threshold (∼ 4 additional cases per year). Sensitivity analyses demonstrated that input parameters used to estimate norovirus concentration accounted for much of the model output variability. This model, while constrained by a lack of knowledge of sewage concentrations, used the best available information and sound logic. Results suggest that current wastewater reuse behaviors in Melbourne are unlikely to cause norovirus risks in excess of the annual DALY health target.