scholarly journals Sensitivity Analysis and Optimization of a Radiative Transfer Numerical Model for Turbid Lake Water

2021 ◽  
Vol 13 (4) ◽  
pp. 709
Author(s):  
JongCheol Pyo ◽  
Yong Sung Kwon ◽  
Jae-Hyun Ahn ◽  
Sang-Soo Baek ◽  
Yong-Hwan Kwon ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Lake Water ◽  
Algal Blooms ◽  
Strong Relationship ◽  
Model Parameters ◽  
Washington Dc ◽  
Backscattering Coefficient ◽  
Parameter Sensitivity Analysis ◽  
Turbid Lake ◽  
Simulation Time

Remote sensing can detect and map algal blooms. The HydroLight (Sequoia Scientific Inc., Bellevue, Washington, DC, USA) model generates the reflectance profiles of various water bodies. However, the influence of model parameters has rarely been investigated for inland water. Moreover, the simulation time of the HydroLight model increases as the amount of input data increases, which limits the practicality of the HydroLight model. This study developed a graphical user interface (GUI) software for the sensitivity analysis of the HydroLight model through multiple executions. The GUI software stably performed parameter sensitivity analysis and substantially reduced the simulation time by up to 92%. The GUI software results for lake water show that the backscattering ratio was the most important parameter for estimating vertical reflectance profiles. Based on the sensitivity analysis results, parameter calibration of the HydroLight model was performed. The reflectance profiles obtained using the optimized parameters agreed with observed profiles, with R2 values of over 0.98. Thus, a strong relationship between the backscattering coefficient and the observed cyanobacteria genera cells was identified.

scholarly journals Spatio-temporal modelling of Leishmania infantum infection among domestic dogs: a simulation study and sensitivity analysis applied to rural Brazil

2018 ◽  
Author(s):  
Elizabeth Buckingham-Jeffery ◽  
Edward M. Hill ◽  
Samik Datta ◽  
Erin Dilger ◽  
Orin Courtenay
Keyword(s):  
Sensitivity Analysis ◽  
Leishmania Infantum ◽  
Parameter Sensitivity ◽  
Domestic Dogs ◽  
Sand Fly ◽  
Model Parameters ◽  
Infection Prevalence ◽  
Parameter Sensitivity Analysis ◽  
Model Framework ◽  
Stochastic Parameter

AbstractBackgroundThe parasite Leishmania infantum causes zoonotic visceral leishmaniasis (VL), a potentially fatal vector-borne disease of canids and humans. Zoonotic VL poses a significant risk to public health, with regions of Latin America being particularly afflicted by the disease.Leishmania infantum parasites are transmitted between hosts during blood feeding by infected female phlebotomine sand flies. With a principal reservoir host of L. infantum being domestic dogs, limiting prevalence in this reservoir may result in a reduced risk of infection for the human population. To this end, a primary focus of research efforts has been to understand disease transmission dynamics among dogs. One way this can be achieved is through the use of mathematical models.MethodsWe have developed a stochastic, spatial, individual-based mechanistic model of L. infantum transmission in domestic dogs. The model framework was applied to a rural Brazilian village setting with parameter values informed by fieldwork and laboratory data. To ensure household and sand fly populations were realistic, we statistically fit distributions for these entities to existing survey data. To identify the model parameters of highest importance, we performed a stochastic parameter sensitivity analysis of the prevalence of infection among dogs to the model parameters.ResultsWe computed parametric distributions for the number of humans and animals per household and a non-parametric temporal profile for sand fly abundance. The stochastic parameter sensitivity analysis determined prevalence of L. infantum infection in dogs to be most strongly affected by the sand fly associated parameters and the proportion of immigrant dogs already infected with L. infantum parasites.ConclusionsEstablishing the model parameters with the highest sensitivity of average L. infantum infection prevalence in dogs to their variation helps motivate future data collection efforts focusing on these elements. Moreover, the proposed mechanistic modelling framework provides a foundation that can be expanded to explore spatial patterns of zoonotic VL in humans and to assess spatially targeted interventions.

Parameter Sensitivity Analysis of the Extended Bouc-Wen Model of Hysteresis

2002 ◽  
Author(s):  
Haochuan Zhang ◽  
Fai Ma
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Simplified Model ◽  
Model Parameters ◽  
Hysteretic Model ◽  
Structural Damping ◽  
Parameter Sensitivity Analysis ◽  
Differential Model ◽  
Global Sensitivity

The extended Bouc-Wen differential model is one of the most widely accepted phenomenological models of hysteresis in computational mechanics. It is routinely used in the characterization of structural damping and in system identification. In this paper, the differential model of hysteresis is carefully re-examined and two significant issues are uncovered. First, it is found that the unspecified parameters of the model are not independent. One of the model parameters can be eliminated through suitable transformations in the parameter space. Second, through local and global sensitivity analysis, it is found that some parameters of the hysteretic model are rather insensitive. If these insensitive parameters are set to constant values, a greatly simplified model is obtained.

Parametric Sensitivity Analysis and Model Tuning Applied to Vapor Compression Systems

2005 ◽  
Author(s):  
Michael C. Keir ◽  
Bryan P. Rasmussen ◽  
Andrew G. Alleyne
Keyword(s):  
Sensitivity Analysis ◽  
System Dynamics ◽  
Model Parameters ◽  
Vapor Compression ◽  
Parameter Sensitivity Analysis ◽  
Model Accuracy ◽  
Vapor Compression System ◽  
Physical Insight ◽  
Model Tuning ◽  
Insight Into

This paper presents a parameter sensitivity analysis for a low-order control-oriented dynamic model of a subcritical vapor compression system. The results are used to tune immeasurable model parameters, account for the unmodeled system dynamics, and are applied to fault detection residual design. The models are validated against data taken from an experimental test stand, and the sensitivity based model tuning is shown to improve the model accuracy while providing enhanced physical insight into system dynamics.

Parameter Sensitivity Analysis and Improvement of a Two-Layer Arterial Wall Model

1983 ◽  
Vol 105 (4) ◽  
pp. 389-392 ◽  
Author(s):  
W. W. von Maltzahn
Keyword(s):  
Sensitivity Analysis ◽  
Energy Density ◽  
Arterial Wall ◽  
Parameter Sensitivity ◽  
Model Parameters ◽  
Parameter Sensitivity Analysis ◽  
Wall Model ◽  
Exponential Relationship ◽  
Validity Analysis ◽  
The Media

The nonlinear two-layer arterial wall model introduced by von Maltzahn, et al. [11] is subjected to a rigorous parameter sensitivity and range of validity analysis. The model is based on the assumption that in large muscular conduit arteries the two mechanically significant layers are media and adventitia. Using curve-fitting techniques, the media is determined to be isotropic and the adventitia to be anisotropic. As a result of the range of validity analysis, the polynomial relationship for the energy density function of the media is changed to an exponential relationship. This leads to new coefficients for the polynomial of the adventitia. All coefficients have specific mechanical meanings. The parameter sensitivity analysis demonstrates convincingly that all model parameters are significantly important.

scholarly journals XBeach Process-Based Modelling of Coastal Morphological Features Near Breakwater

2018 ◽  
Vol 203 ◽  
pp. 01007 ◽  
Author(s):  
Mohd Shahrizal Ab Razak ◽  
Nur Arriffah Zaimah Mohd Nor
Keyword(s):  
Sensitivity Analysis ◽  
Empirical Models ◽  
Coastal Protection ◽  
Model Parameters ◽  
Effective Role ◽  
Optimum Model ◽  
Coastal Features ◽  
Simulation Time

An effective role of a detached breakwater as a coastal protection structure leads to morphological evolutions of tombolo. This paper presents the application of process based model of XBeach to investigate the morphological tombolo evolution using the model domain from the previous case study. Sensitivity analysis with optimum model parameters such as facua 0.1, Chezy coefficient 60, directional energy distribution, dtheta 10 and morfac 100 is conducted and the model results are compared to the empirical models. At 50 and 75 days simulation time, XBeach model results for breakwater at distance 120 m, 150 m, 200 m and 300 m offshore forms tombolo and salient at a distance of 500 m from the shoreline. Numerical investigation of tombolo through XBeach model has given us an understanding on the morphological effects of breakwater offshore distance towards evolution of coastal features like tombolo and salient.

A modeling approach to simulate impact of climate change in lake water quality: phytoplankton growth rate assessment

1998 ◽  
Vol 37 (2) ◽  
pp. 177-185 ◽  
Author(s):  
Hany Hassan ◽  
Keisuke Hanaki ◽  
Tomonori Matsuo
Keyword(s):  
Climate Change ◽  
Water Quality ◽  
Growth Rate ◽  
Dissolved Oxygen ◽  
Lake Water ◽  
Phytoplankton Growth ◽  
Quality Data ◽  
Model Parameters ◽  
Lake Water Quality ◽  
Phytoplankton Growth Rate

Global climate change induced by increased concentrations of greenhouse gases (especially CO2) is expected to include changes in precipitation, wind speed, incoming solar radiation, and air temperature. These major climate variables directly influence water quality in lakes by altering changes in flow and water temperature balance. High concentration of nutrient enrichment and expected variability of climate can lead to periodic phytoplankton blooms and an alteration of the neutral trophic balance. As a result, dissolved oxygen levels, with low concentrations, can fluctuate widely and algal productivity may reach critical levels. In this work, we will present: 1) recent results of GCMs climate scenarios downscaling project that was held at the University of Derby, UK.; 2) current/future comparative results of a new mathematical lake eutrophication model (LEM) in which output of phytoplankton growth rate and dissolved oxygen will be presented for Suwa lake in Japan as a case study. The model parameters were calibrated for the period of 1973–1983 and validated for the period of 1983–1993. Meterologic, hydrologic, and lake water quality data of 1990 were selected for the assessment analysis. Statistical relationships between seven daily meteorological time series and three airflow indices were used as a means for downscaling daily outputs of Hadley Centre Climate Model (HadCM2SUL) to the station sub-grid scale.

scholarly journals A Sensitivity Analysis of the SPACSYS Model

Agriculture ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 624
Author(s):  
Yan Shan ◽  
Mingbin Huang ◽  
Paul Harris ◽  
Lianhai Wu
Keyword(s):  
Sensitivity Analysis ◽  
Wheat Yield ◽  
Climatic Conditions ◽  
Water Dynamics ◽  
Ecological Modelling ◽  
Soil Conditions ◽  
Model Parameters ◽  
Nutrient Inputs ◽  
Nutrient Pool ◽  
Wheat Field

A sensitivity analysis is critical for determining the relative importance of model parameters to their influence on the simulated outputs from a process-based model. In this study, a sensitivity analysis for the SPACSYS model, first published in Ecological Modelling (Wu, et al., 2007), was conducted with respect to changes in 61 input parameters and their influence on 27 output variables. Parameter sensitivity was conducted in a ‘one at a time’ manner and objectively assessed through a single statistical diagnostic (normalized root mean square deviation) which ranked parameters according to their influence of each output variable in turn. A winter wheat field experiment provided the case study data. Two sets of weather elements to represent different climatic conditions and four different soil types were specified, where results indicated little influence on these specifications for the identification of the most sensitive parameters. Soil conditions and management were found to affect the ranking of parameter sensitivities more strongly than weather conditions for the selected outputs. Parameters related to drainage were strongly influential for simulations of soil water dynamics, yield and biomass of wheat, runoff, and leaching from soil during individual and consecutive growing years. Wheat yield and biomass simulations were sensitive to the ‘ammonium immobilised fraction’ parameter that related to soil mineralization and immobilisation. Simulations of CO2 release from the soil and soil nutrient pool changes were most sensitive to external nutrient inputs and the process of denitrification, mineralization, and decomposition. This study provides important evidence of which SPACSYS parameters require the most care in their specification. Moving forward, this evidence can help direct efficient sampling and lab analyses for increased accuracy of such parameters. Results provide a useful reference for model users on which parameters are most influential for different simulation goals, which in turn provides better informed decision making for farmers and government policy alike.

scholarly journals Global Sensitivity Analysis of a Homogenized Constrained Mixture Model of Arterial Growth and Remodeling

2021 ◽  
Author(s):  
Sebastian Brandstaeter ◽  
Sebastian L. Fuchs ◽  
Jonas Biehler ◽  
Roland C. Aydin ◽  
Wolfgang A. Wall ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Model Parameters ◽  
Computational Studies ◽  
Growth And Remodeling ◽  
Global Sensitivity ◽  
Output Variability ◽  
Constrained Mixture ◽  
The One ◽  
Influential Parameters

AbstractGrowth and remodeling in arterial tissue have attracted considerable attention over the last decade. Mathematical models have been proposed, and computational studies with these have helped to understand the role of the different model parameters. So far it remains, however, poorly understood how much of the model output variability can be attributed to the individual input parameters and their interactions. To clarify this, we propose herein a global sensitivity analysis, based on Sobol indices, for a homogenized constrained mixture model of aortic growth and remodeling. In two representative examples, we found that 54–80% of the long term output variability resulted from only three model parameters. In our study, the two most influential parameters were the one characterizing the ability of the tissue to increase collagen production under increased stress and the one characterizing the collagen half-life time. The third most influential parameter was the one characterizing the strain-stiffening of collagen under large deformation. Our results suggest that in future computational studies it may - at least in scenarios similar to the ones studied herein - suffice to use population average values for the other parameters. Moreover, our results suggest that developing methods to measure the said three most influential parameters may be an important step towards reliable patient-specific predictions of the enlargement of abdominal aortic aneurysms in clinical practice.

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