Global sensitivity analysis of PROSAIL model parameters when simulating Moso bamboo forest canopy reflectance

Local and global sensitivity analysis (SA) methods were compared to demonstrate how the choice of SA method leads to differences in the quantification of the relative importance of model parameters in driving canopy reflectance variability. The global SA method used was the Extended Fourier Amplitude Sensitivity Test (EFAST), while a series of basecase simulations were defined for the local SA, and model parameters were perturbed sequentially. Canopy reflectance was simulated using the ProGOSAIL model. Some pronounced differences were found between the two methods in terms of the magnitude of the importance, the rank importance, and the wavelengths at which model parameters are most important in explaining reflectance variability.

To provide a reference for canopy parameters inversion, sensitivity analysis of plant canopy parameters based on remote sensing model is a prerequisite for the inversion. Because the local sensitivity analysis do not consider the coupling effect among the parameters, the EFAST (i.e., Extended Fourier Amplitude Sensitivity Test), a global sensitivity analysis, can be used not only for the analysis of each parameter, but also consider the interacted effect among each parameter. Based on PROSAIL model, the paper focused on the parameters’ sensitivity by using simulated data and EFAST method. The results showed that the EFAST considered not only the contribution of single parameter, but also the interactive effects among each parameter, and four parameters, leaf area index (LAI), leaf mesophyll structure (N), the controller factor of the average leaf slope (LIDFa) and soil moisture condition (psoil) had great effect on the canopy reflectance in the whole wavelength from 400 to 2500 nm than other canopy parameters, and the EFAST method enlarged the contribution of some parameters that had little effects.

Parameter estimation is a major challenge for mathematical modelling of biological systems. Given the uncertainties associated with model parameters, it is important to understand how sensitive the model output is to variations in parameter values. A local sensitivity analysis determines the model sensitivity to parameter variations over a localised region around the nominal parameter values, whereas a global sensitivity analysis (GSA) investigates the sensitivity over the entire parameter space. Using a T-cell receptor-activated Erk-MAPK signalling pathway model as an example, the authors present a comparative study of a variety of different sensitivity analysis techniques. These techniques include: local sensitivity analysis, existing GSA methods of partial rank correlation coefficient, Sobol's, extended Fourier amplitude sensitivity test, as well as a weighted average of local sensitivities and a new GSA method to extract global parameter sensitivities from a parameter identification routine. Results of this study revealed critical reactions in the signalling pathway and their impact on the signalling dynamics and provided insights into embedded regulatory mechanisms such as feedback loops in the pathway. From this study, a recommendation emerges for a general sensitivity analysis strategy to efficiently and reliably infer quantitative, dynamic as well as topological properties from systems biology models.

Copula-based methods for global sensitivity analysis with correlated random variables and stochastic processes under incomplete probability information

Simulation of fate and transport mechanisms in the porous systems from contaminant transport models are affected by uncertainty associated with input model parameters. Sensitivity analysis (SA) provides tools to quantify the sources of uncertainty to the variations in the model output metrics. To quantify the uncertainty associated with physical non-equilibrium contaminant transport model, global SA was conducted for the problem mimicking reactive transport in the saturated soil column conditions. Five global SA methods, namely Morris, RSA (Regionalized Sensitivity Analysis), Sobol, FAST (Fourier Amplitude Sensitivity Test), and PAWN, were tested based on the temporal moments of contaminant concentrations for two output metrics: zeroth temporal moment (ZTM) and mean residence time (MRT). The ranking order of ten input model parameters from global SA methods for two output metrics was compared. Morris SA implied that the ZTM at outlet of the soil column is highly sensitive to sorption distribution coefficient in the mobile and immobile regions and less sensitive to dispersion coefficient and degradation rate constant in the immobile region. The mass-transfer coefficient showed highest non-linear interactions with other flow and transport parameters based on the highest value of standard deviation of elementary effects (EEs) for ZTM output metric. The sorption distribution coefficient in the mobile region and mass-transfer coefficient showed highest sensitivity and non-linear effect toward MRT based on the Morris method. The comparison of global SA methods revealed that the top two sensitive parameters affecting ZTM and MRT were the same from all the considered methods. However, large difference in the ranking order of bottom three sensitive parameters was observed. The sorption distribution coefficient of mobile region and mass-transfer coefficient were observed as the most sensitive parameters affecting ZTM and MRT based on the comparison of all five global SA methods. Overall results suggest that the non-linear contaminant transport model should be examined based on multiple sensitivity output metrics via a multi-model approach. The multi-model global SA approach implemented in this study highlighted its significance in quantifying the interplay of non-linear model parameters.

Development of a behaviour-pattern based global sensitivity analysis procedure for coupled socioeconomic and environmental models

This paper presents an application of the Global Sensitivity Analysis (GSA) method in evaluating performance of impedance measurement algorithms implemented in modern Intelligent Electronic Devices (IEDs) when affected by uncertainty. To be specific, the GSA is applied in testing an algorithm for distance protection of a single transmission line in a system with two lines on the same towers. Factors contributing to uncertainty are discussed in the paper. The GSA method decomposes variance of the estimation error according to the factors and their interactions, and in this way identifies the factors that have the highest impact on the estimation error. Computation of the GSA sensitivity indices relies on the Quasi-Monte Carlo (QMC) technique with Sobol's quasi-random sampling. This is time-consuming for large number of factors therefore, fast pre-screening process based on the Morris method is implemented to remove non-influential parameters and reduce the factor space before running the QMC. The methodology is implemented using fault simulation software DIgSILENT PowerFactory to calculate fault impedance as a function of uncertain parameters (i.e. factors) as well as voltage and current signals that are inputs to an IED under test. In this simulation, factor space is sampled using Sobol's quasi-random sequence generated with the standard GSA software tool called SIMLAB. All simulations of the transmission line faults for various parameter values and calculation of the performance indices (i.e. sensitivities) is automated using the program developed in DIgSILENT Programming Language (DPL). This testing technique is applied to study the fault impedance measurement implemented in SEL-421. (6 pages)

Comparison of Landsat TM and ERS-2 SAR data for discriminating among grassland types and treatments in eastern Kansas

Uncertainty and sensitivity analysis of cooling and heating loads for building energy planning

Physiologically based pharmacokinetic (PBPK) models describe the absorption, distribution, metabolism, and excretion of chemicals. Probabilistic PBPK models can be used to produce distributional estimates of human equivalent doses (HEDs), which are external measures of human exposure predicted to result in a target internal dose, generated with Monte Carlo sampling and reverse dosimetry calculations. Very low HED percentiles represent individuals that are more sensitive to possible adverse effects of chemical exposures and are therefore frequently used in risk evaluation. Details of the parameter distributions used in probabilistic PBPK models impact HED distributions, but obtaining precise distributional estimates for all the parameters would be challenging. Therefore, we sought to determine methods that can identify the extent of model parameter influence on extreme HED percentiles. We first analyzed published PBPK models for dichloromethane and chloroform (for inhalation and oral exposures given 3 internal target levels each) by identifying the overall relative importance of parameters using global sensitivity analysis (GSA) methods. Then, we used a novel yet computationally expensive method to analyze the stability and sensitivity of extreme HED percentiles to input parameter distributions. Applying the traditional GSA methods allowed us to identify subsets of parameters most influential for accurately determining 1st and 99th percentiles, but the specific parameters included in those subsets varied for different models and exposure scenarios. Our results demonstrate that better characterizing PBPK model uncertainty by using precise distributional details for influential parameters informed by GSA methods may improve confidence in estimates of extreme percentiles of HEDs.

Intelligent sensitivity analysis framework based on machine learning for spacecraft thermal design

In the future, data from the moderate resolution imaging spectroradiometer (MODIS) will be employed to map snow in an automated environment at a resolution of 250 m to 1 km. Using Landsat thematic mapper (TM) data, an algorithm, SNOMAP, has been developed to map snow-covered area. This algorithm will be used, with appropriate modification, with MODIS data following the launch of the first Earth Observing System (EOS) platform in 1998. SNOMAP has been shown to be successful in mapping snow in a variety of areas using TM data. However, significant errors may be present in mountainous areas due to effects of topography. To increase the accuracy of mapping global snow-covered area in the future using MODIS data, digital elevation model (DEM) data have been registered to TM data for parts of Glacier National Park, Montana, so that snow cover on mountain slopes can be mapped. This paper shows that the use of DEM data registered to TM data increases the accuracy of mapping snow-covered area. Using SNOMAP on a subscene within the 14 March 1991 TM scene of northwestern Montana, 215 km2 of snow is mapped when TM data are used alone to map the snow cover. We show that about 1062 km2 of snow are actually present as measured when the TM and DEM data are registered. Approximately five times more snow is present when the effects of topography are considered for this subscene, which is in a rugged area in Glacier National Park. A simple model has been developed to determine the relationship between terrain relief and the amount of correction that must be applied to map actual snow-covered area in Glacier National Park using satellite data alone.

In this paper, we study the dynamical effects of timely and delayed diagnosis on the spread of COVID-19 in Ghana during its initial phase by using reported data from March 12 to June 19, 2020. The estimated basic reproduction number, ℛ0, for the proposed model is 1.04. One of the main focus of this study is global stability results. Theoretically and numerically, we show that the disease persistence depends on ℛ0. We carry out a local and global sensitivity analysis. The local sensitivity analysis shows that the most positive sensitive parameter is the recruitment rate, followed by the relative transmissibility rate from the infectious with delayed diagnosis to the susceptible individuals. And that the most negative sensitive parameters are: self-quarantined, waiting time of the infectious for delayed diagnosis and the proportion of the infectious with timely diagnosis. The global sensitivity analysis using the partial rank correlation coefficient confirms the directional flow of the local sensitivity analysis. For public health benefit, our analysis suggests that, a reduction in the inflow of new individuals into the country or a reduction in the inter community inflow of individuals will reduce the basic reproduction number and thereby reduce the number of secondary infections (multiple peaks of the infection). Other recommendations for controlling the disease from the proposed model are provided in Section 7.

Aircraft manufacturing companies have to consider multiple derivatives to satisfy various market requirements. They modify or extend an existing aircraft to meet the new market demands while keeping the development time and the cost to a minimum. Many researchers have studied the derivative design process, but these research considered the baseline and the derivatives together, while using the whole set of design variables. Therefore, an efficient process that can reduce the cost and the time for the aircraft derivative design is needed. In this dissertation, Aircraft Derivative Design Optimization process (ADDOPT) was developed which obtains the global changes from the local changes in the aircraft design to develop the aircraft derivatives efficiently. The sensitivity analysis was implemented to ignore design variables that have low impact on the objective function. This avoids wasting computational effort and time on low priority variables for design requirements and objectives. Additionally, the classification of uncertainty from its characteristics and sources of uncertainty involved in the aircraft design process were suggested to consider with design optimization. Uncertainty from the fidelity of analysis tools was applied in design optimization to increase the probability of optimization results. To handle uncertainty in low fidelity analysis tools on aircraft conceptual design optimization, Reliability Based Design Optimization (RBDO) and Possibility Based Design Optimization (PBDO) methods were performed. In this research, Extended Fourier Amplitude Sensitivity Test (eFAST) method was implemented in ADDOPT for Global Sensitivity Analysis (GSA) method and Collaborative Optimization (CO) based framework with RBDO and PBDO were also used. These methods were evaluated using numerical examples. ADDOPT was carried through on the civil jet aircraft derivative design. The objective of the optimization problem was to increase cruise range while satisfying the requirement such as the number of passengers. The proposed process reduced computation effort by reducing the number of design variables and achieved the target probability of failure when considering uncertainty from low fidelity analysis tools.

Digital Landsat Thematic Mapper (TM) observations, multi-frequency polarimetric AIRSAR radar data, microtopographic profiles derived from helicopter-borne photography, and field measurements were analyzed over the Pisgah and Cima volcanic fields, and Lavic Lake playa, Mojave Desert, California. Surface radiance factors were extracted from TM data using radiative transfer procedures to model the atmosphere and surface simultaneously. AIRSAR data were calibrated to backscatter specific cross sections using corner reflectors deployed at a number of locations. Analyses focused on the use of key field sites to understand compositional and textural controls on TM band-5 (1.55 to 1.75 μm) radiance factors and C- (5.6 cm), L- (24 cm), and P- (68 cm) band specific cross sections. For the Pisgah and Lavic Lake sites, the TM data are explained using a linear mixing model of basalt and fine-grained sediment (clay to sand-sized grains of clay minerals, quartz, feldspar, carbonate, and lithic fragments) radiance factors, with the addition of shadows for the rough a9a flow. On the other hand, AIRSAR data are shown to be primarily controlled by surface roughness, as there is a strong correlation between specific cross section and surface height standard deviation. For the elongate flows at the Cima volcanic field, which exhibit a mix of pahoehoe and a9a properties, we find a positive linear relationship between TM band-5 radiance factors and flow ages for the 3 m.y. age range represented. Specific cross sections remain constant until about 0.14 Ma and then decrease with age up to about 0.56 Ma. On the other hand, data for the two older flows (0.70 and 0.85 Ma) show increasing specific cross sections with increasing age. We interpret the increase in TM band-5 radiance factors (from 0.05 to 0.25) with increasing flow age and the decrease in specific cross sections (8 dB change at 35° incidence angle for L band) for flows up to 0.56 Ma in age as due to smoothing by mass wasting and infill by fine-grained aeolian deposits. The increased specific cross sections for older flows are interpreted to be a consequence of surface roughness generated by fluvial dissection.