Error propagation in computer models: analytic approaches, advantages, disadvantages and constraints

Benke, Kurt K.; Norng, S.; Robinson, Nathan; Benke, Lyndon; Peterson, T. J.

doi:10.1007/s00477-018-1555-8

Cited by 21 publications

(33 citation statements)

References 55 publications

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“…The results were very encouraging given that there were potential limitations relating to data quality, including the period of time covering data acquisition (from 1948), different measurement and field techniques used and different operators. There were also epistemic uncertainties, including factors such as depth resolution of the control section, data veracity due to data entry errors and measurement error (Biggs and Searle, 2017;Benke et al, 2018). The signals (predictors) identified in the presence of noisy data implies a significant level of robustness in the information produced.…”

Section: Discussionmentioning

confidence: 99%

“…This method is repeated five times with all five subsets used in the validation of the model. All the data were used in the initial exploratory experiments to identify potential pair-wise relationships between covariates, to aid in model development, and therefore there exists a degree of epistemic uncertainty due to sampling (Robinson et al, 2015;Benke et al, 2018). The prediction error was calculated using the standard metrics, i.e.…”

Section: Model Prediction Errormentioning

confidence: 99%

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Development of pedotransfer functions by machine learning for prediction of soil electrical conductivity and organic carbon content

et al. 2020

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The pedotransfer function is a mathematical model used to convert direct soil measurements into known and unknown soil properties. It provides information for modelling and simulation in soil research, hydrology, environmental science and climate change impacts, including investigating the carbon cycle and the exchange of carbon between soils and the atmosphere to support carbon farming. In particular, the pedotransfer function can provide input parameters for landscape design, soil quality assessment and economic optimisation. The objective of the study was to investigate the feasibility of using a generalised pedotransfer function derived with a machine learning method to predict soil electrical conductivity (EC) and soil organic carbon content (OC) for different regional locations in the state of Victoria, Australia. This strategy supports a unified approach to the interpolation and population of a single regional soils database, in contrast to a range of pedotransfer functions derived from local databases

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Section: Discussionmentioning

confidence: 99%

Section: Model Prediction Errormentioning

confidence: 99%

Development of pedotransfer functions by machine learning for prediction of soil electrical conductivity and organic carbon content

et al. 2020

Self Cite

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“…For example, Figure shows that using the Disley et al () equation, the predicted k x could be anywhere between approximately 0 and 10 times the “possible real k x ,” so dividing the predicted k x by each of the randomly drawn Pr values would give 2,000 possible real k x values. A straightforward Monte Carlo simulation was deemed the most suitable approach, due to its conceptual simplicity as well as its ease of explanation to, for example, regulators (Benke et al, ; Helton, ; Sriwastava et al, ). Using an analytical solution of the 1D ADE, given by equation below (Rutherford, ), and the “possible real k x values” (based on the drawn Pr value from step 2), the downstream concentration profile located at 3.8 km was calculated. This was achieved by successively routing the observed upstream concentration profile (at 2.5 km) over each subreach until the concentration profile at the last subreach was obtained (utilizing the geometric and hydraulic data).…”

Section: Methodology For Uncertainty Propagationmentioning

confidence: 99%

“…This section presents a methodology to evaluate the uncertainty within water quality modeling due to the selection of the dispersion coefficient using the equations detailed in Table , utilizing a data set from an independent dye‐tracing experiment conducted in the Chillan River. A general background description and discussion of uncertainty propagation using Monte Carlo methods within environmental modeling can be found in, for example, Benke et al () and Helton (). The Chillan River field study has been selected as the data have not been included in the derivation of any of the studied dispersion coefficient equations and because field survey and solute concentration versus time profiles are available, measured downstream of an instantaneous release of tracer.…”

Section: Methodology For Uncertainty Propagationmentioning

confidence: 99%

“…For example, Figure 2 shows that using the Disley et al (2015) equation, the predicted k x could be anywhere between approximately 0 and 10 times the "possible real k x ," so dividing the predicted k x by each of the randomly drawn Pr values would give 2,000 possible real k x values. A straightforward Monte Carlo simulation was deemed the most suitable approach, due to its conceptual simplicity as well as its ease of explanation to, for example, regulators (Benke et al, 2018;Helton, 1993;Sriwastava et al, 2018).…”

Section: 1029/2018wr023417mentioning

confidence: 99%

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Quantifying the Impact of Uncertainty within the Longitudinal Dispersion Coefficient on Concentration Dynamics and Regulatory Compliance in Rivers

Suarez

Schellart

Brevis

et al. 2019

Water Resources Research

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The one‐dimensional advection dispersion equation (1D ADE) is commonly used in practice to simulate pollutant transport processes for assessment and improvement of water quality conditions in rivers. Various studies have shown that the longitudinal dispersion coefficient used within the 1D ADE is influenced by a range of hydraulic and geomorphological conditions. This study aims to quantify the impact and importance of the parameter uncertainty associated with the longitudinal dispersion coefficient on modeled pollutant time‐concentration profiles and its implications for meeting compliance with water quality regulations. Six regression equations for estimating longitudinal dispersion coefficients are evaluated, and commonly used evaluation criteria were assessed for their suitability. A statistical evaluation of the regression equations based on their original calibration data sets resulted in percent bias (PBIAS) values between −47.01% and 20.78%. For a case study, uncertainty associated with the longitudinal dispersion coefficient was propagated to time‐concentration profiles using 1D ADE and Monte Carlo simulations, and 75% confidence interval bands of the pollutant concentration versus time profiles were derived. For two studied equations, the measured peak concentration values were above the simulated 87.5th percentile, and for the other four equations it was close to the 87.5th percentile. Subsequent uncertainty propagation analysis of four diverse rivers show the potential considerable impact on concentration‐duration‐frequency‐based water quality studies, with 1D ADE modeling producing predictions of quality standard compliance which varied over hundreds of kilometers.

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Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review

2023

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A review of uncertainty quantification techniques is provided for a variety of situations involving uncertainties in model inputs (independent variables). The situations of interest are divided into three categories: (i) when model prediction uncertainties are quantified based on uncertainties in uncertain inputs, (ii) when parameter estimate uncertainties are calculated by propagation of uncertainties from measured inputs and outputs, and (iii) when model prediction uncertainties are quantified based on corresponding uncertainties in measured inputs and uncertain parameter estimates. For all three situations, linearization‐based and Monte Carlo‐based techniques are reviewed and details for their corresponding algorithms are presented. Recommendations are provided on which uncertainty quantification techniques are best for different types of chemical engineering models based on the amount of input uncertainty and nonlinearity over the range of plausible input and parameter values.

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Error propagation in computer models: analytic approaches, advantages, disadvantages and constraints

Cited by 21 publications

References 55 publications

Development of pedotransfer functions by machine learning for prediction of soil electrical conductivity and organic carbon content

Development of pedotransfer functions by machine learning for prediction of soil electrical conductivity and organic carbon content

Quantifying the Impact of Uncertainty within the Longitudinal Dispersion Coefficient on Concentration Dynamics and Regulatory Compliance in Rivers

Propagating input uncertainties into parameter uncertainties and model prediction uncertainties—A review

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