Evaluation of Four GLUE Likelihood Measures and Behavior of Large Parameter Samples in ISPSO-GLUE for TOPMODEL

Cho, Huidae; Park, Jeongha; Kim, Dongkyun

doi:10.3390/w11030447

Cited by 7 publications

(4 citation statements)

References 43 publications

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“…Therefore, only simulated subjects with the selected NRMSE threshold of 20% were considered relevant to the test subject and included in the envelope when compared to the observed HR response. This is consistent with prior studies in defining behavioral samples for model evaluation using likelihood measures that evaluates the fitness of the model to the observed data, e.g., generalized likelihood uncertainty estimation (GLUE) 37 . This NRMSE threshold can be adjusted for other applications or physiological variables.…”

Section: Model Validationsupporting

confidence: 87%

Development and validation of a mathematical model of heart rate response to fluid perturbation

Kanal

Pathmanathan

Hahn

et al. 2022

Sci Rep

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Physiological closed-loop controlled (PCLC) medical devices monitor and automatically adjust the patient’s condition by using physiological variables as feedback, ideally with minimal human intervention to achieve the target levels set by a clinician. PCLC devices present a challenge when it comes to evaluating their performance, where conducting large clinical trials can be expensive. Virtual physiological patients simulated by validated mathematical models can be utilized to obtain pre-clinical evidence of safety and assess the performance of the PCLC medical device during normal and worst-case conditions that are unlikely to happen in a limited clinical trial. A physiological variable that plays a major role during fluid resuscitation is heart rate (HR). For in silico assessment of PCLC medical devices regarding fluid perturbation, there is currently no mathematical model of HR validated in terms of its predictive capability performance. This paper develops and validates a mathematical model of HR response using data collected from sheep subjects undergoing hemorrhage and fluid infusion. The model proved to be accurate in estimating the HR response to fluid perturbation, where averaged between 21 calibration datasets, the fitting performance showed a normalized root mean square error (NRMSE) of $$7.41 \pm 2.8 \%$$ 7.41 ± 2.8 % . The model was also evaluated in terms of model predictive capability performance via a leave-one-out procedure (21 subjects) and an independent validation dataset (6 subjects). Two different virtual cohort generation tools were used in each validation analysis. The generated envelope of virtual subjects robustly met the defined acceptance criteria, in which $$95\%$$ 95 % of the testing datasets presented simulated HR patterns that were within a deviation of 50% from the observed data. In addition, out of 16000 and 18522 simulated subjects for the leave-one-out and independent datasets, the model was able to generate at least one virtual subject that was close to the real subject within an error margin of $$9.56 \pm 3.15\%$$ 9.56 ± 3.15 % and $$11.1 \pm 1.22\%$$ 11.1 ± 1.22 % NRMSE, respectively. In conclusion, the model can generate valid virtual HR physiological responses to fluid perturbation and be incorporated into future non-clinical simulated testing setups for assessing PCLC devices intended for fluid resuscitation.

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Section: Model Validationsupporting

confidence: 87%

Development and validation of a mathematical model of heart rate response to fluid perturbation

Kanal

Pathmanathan

Hahn

et al. 2022

Sci Rep

View full text Add to dashboard Cite

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“…2011; Cho et al. 2019). None of the coupled options had such a narrow range of “ m ” parameter value.…”

Section: Resultsmentioning

confidence: 99%

“…2011; Cho et al. 2019). Random sampling methods were applied to perform model runs and parameter sensitivity analysis based on parameter bounds.…”

Section: Methodsmentioning

confidence: 99%

Experimental Coupling of TOPMODEL with the National Water Model: Effects of Coupling Interface Complexity on Model Performance

Kim

Naliaka

Zhu

et al. 2021

J American Water Resour Assoc

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The study had two objectives; (1) Substitute National Water Model’s (NWM) runoff calculation with a conceptual hydrologic model (TOPography‐based hydrological MODEL [TOPMODEL]) to simplify the model structure and resolve potential drawbacks of applying NWM in headwater catchments. (2) Investigate how varying the coupling interface (location of coupling, type of fluxes used, modification of sub‐models) affects model behavior of when one‐way coupling the NWM’s land surface model (LSM; Noah‐Multi Parameterization) with TOPMODEL using six different scenarios. The one‐way coupled model outperformed NWM and noncoupled TOPMODEL. The coupling option limiting reliance on LSM’s surface and subsurface water fluxes by constraining them within the TOPMODEL structure was the most successful. Performance declined when coupling configurations relied more on LSM calculated fluxes to override TOPMODEL internal processes. Varying the coupling interface brought unexpected changes in TOPMODEL’s parameter sensitivity and water budget even while the statistical score remained similar. The coupling interface represents a source of structural uncertainty that could be identified through conventional evaluation of performance, uncertainty, and sensitivity due to the simple structure of our one‐way coupling design. The study shows that the benefits of combining the strengths of land surface and conceptual hydrological models, while recognizing that structural uncertainty from coupling design needs to be acknowledged.

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“…These three studies for sensitivity analysis [1,5,6] were carried out in order to investigate the uncertainty of parameters and model structure. Beven and Freer [7] and Cho et al [8] investigated the uncertainty of rainfall-runoff models using the Generalized Likelihood Uncertainty Estimation (GLUE) method, and Wagener et al [9] evaluated the structure of rainfall-runoff models by identifying the model parameters using the Dynamic Identifiability Analysis (DYNIA) method. Vrugt et al [10] investigated the improvement of the structure of rainfall-runoff models by calculating the boundary of the uncertainty of simulation results using the Shuffled Complex Evolution Metropolis Algorithm (SCEM-UA).…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Effect of Uncertainty in Rainfall-Runoff Models on Simulation Results Using a Simple Uncertainty-Screening Method

Shin¹,

Kim

2019

Water

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Various uncertainty analysis methods have been used in various studies to analyze the uncertainty of rainfall-runoff models; however, these methods are difficult to apply immediately as they require a long learning time. In this study, we propose a simple uncertainty-screening method that allows modelers to investigate relatively easily the uncertainty of rainfall-runoff models. The 100 best parameter values of three rainfall-runoff models were extracted using the efficient sampler DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm, and the distribution of the parameter values was investigated. Additionally, the ranges of the values of a model performance evaluation statistic and indicators of hydrologic alteration corresponding to the 100 parameter values for the calibration and validation periods was analyzed. The results showed that the Sacramento model, which has the largest number of parameters, had uncertainties in parameters, and the uncertainty of one parameter influenced all other parameters. Furthermore, the uncertainty in the prediction results of the Sacramento model was larger than those of other models. The IHACRES model had uncertainty in one parameter related to the slow flow simulation. On the other hand, the GR4J model had the lowest uncertainty compared to the other two models. The uncertainty-screening method presented in this study can be easily used when the modelers select rainfall-runoff models with lower uncertainty.

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Evaluation of Four GLUE Likelihood Measures and Behavior of Large Parameter Samples in ISPSO-GLUE for TOPMODEL

Cited by 7 publications

References 43 publications

Development and validation of a mathematical model of heart rate response to fluid perturbation

Development and validation of a mathematical model of heart rate response to fluid perturbation

Experimental Coupling of TOPMODEL with the National Water Model: Effects of Coupling Interface Complexity on Model Performance

Analysis of the Effect of Uncertainty in Rainfall-Runoff Models on Simulation Results Using a Simple Uncertainty-Screening Method

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