Sensitivity Analysis Of Model Research Articles

Sensitivity analysis of a 2D flood inundation model. A case study of Tous Dam

Sensitivity analysis of a 2D flood inundation model. A case study of Tous Dam

Participatory assessment of pollution and health risk in artisanal and small-scale gold mining communities in Colombia

The predominantly Afro-descendant communities of Yolombó and La Toma depend on the region's rich natural resources through livelihoods such as farming, fishing, and artisanal and small-scale gold mining (ASGM). Previous studies of the region have identified elevated levels of mercury in several surface and groundwater sites. Furthermore, recent regional studies have identified human health risks associated with mercury exposure through hazard quotient (HQ) modeling and global sensitivity and uncertainty analysis (GSUA). However, there has yet to be a comprehensive analysis of water pollution involving different metals and metalloids potentially present in ASGM-influenced areas to estimate the associated environmental and human health impacts. In this study, we employed a participatory methodology to identify sources of exposure in Colombian ASGM communities through surveys, consumption diaries, and interviews. We then screened for relevant metals and metalloids through groundwater analysis using inductively coupled plasma optical emission spectroscopy (ICP-OES) and cold vapor atomic absorption spectrometry (CV-AAS). Furthermore, we calculated water pollution indices, including heavy metal pollution index (HPI), degree of contamination (Cd), heavy metal toxicity load (HMTL) and heavy metal evaluation index (HEI). Finally, we quantified human health risks related to groundwater consumption through the HQ and hazard index (HI). Although the groundwater assessed in this study generally meets United States and Colombian regulatory standards, heightened Fe, Mn, and Al levels were observed. The pollution indices generally agreed that three of the sites tested showed medium to high pollution levels. The HI showed that the combined effect of all metals tested did not substantiate risk for most participants. However, the HI of one participant, a child, exceeded the 1.00 threshold. Furthermore, the presence of Pb, despite meeting regulatory limits, necessitates further scrutiny due to its substantial impact on pollution and human health hazard indices.

Bayesian probabilistic sensitivity analysis of Markov models for natural history of a disease: an application for cervical cancer

Background: parameter uncertainty in the Markov model’s description of a disease course was addressed. Probabilistic sensitivity analysis (PSA) is now considered the only tool that properly permits parameter uncertainty’s examination. This consists in sampling values from the parameter’s probability distributions. Methods: Markov models fitted with microsimulation were considered and methods for carrying out a PSA on transition probabilities were studied. Two Bayesian solutions were developed: for each row of the modeled transition matrix the prior distribution was assumed as a product of Beta or a Dirichlet. The two solutions differ in the source of information: several different sources for each transition in the Beta approach and a single source for each transition from a given health state in the Dirichlet. The two methods were applied to a simple cervical cancer’s model. Results : differences between posterior estimates from the two methods were negligible. Results showed that the prior variability highly influence the posterior distribution. Conclusions: the novelty of this work is the Bayesian approach that integrates the two distributions with a product of Binomial distributions likelihood. Such methods could be also applied to cohort data and their application to more complex models could be useful and unique in the cervical cancer context, as well as in other disease modeling.

Spreadsheet Modeling and Wrangling with Python

A staple of many spreadsheet-based management science courses is the use of Excel for activities such as model building, sensitivity analysis, goal seeking, and Monte-Carlo simulation. What might those things look like if carried out using Python? We describe a teaching module in which Python is used to do typical Excel-based modeling and data-wrangling tasks. In addition, students are exposed to basic software engineering principles, including project folder structures, version control, object-oriented programming, and other more advanced Python skills, creating deployable packages and documentation. The module is supported with Jupyter notebooks, Python scripts, course web pages that include numerous screencasts, and a few GitHub repositories. All of the supporting materials are permissively licensed and freely accessible. Supplemental Material: The supplemental files are available at https://doi.org/10.1287/ited.2023.0047 .

Sensitivity of bias correction step on generating hydrological scenarios

ABSTRACT Significant shifts in hydro-climatic regimes are expected in many parts of the world during the 21st century, affecting the water cycle. Vulnerability, impact, and adaptation studies often use tailored modeling chains to assess the expected effects of climate change, but the robustness of these chains is rarely investigated. This highlights the need for more rigorous evaluation of modeling chains to ensure that they are reliable for informed decision-making processes. To address this gap, we propose a framework for evaluating the sensitivity of hydrological scenario production to the bias correction step. We apply the framework to the Senegal River Basin, using three bias correction methods (linear scale, empirical quantile mapping, and nested bias correction) and three procedures (climate-correction, hydrological-correction, and climate-hydrological-correction). Our results show that the choice of modeling chain has a significant impact on future hydro-climatic trajectories. In particular, the combination of climate-and-hydrological-correction procedures may be optimal when both climate biases and hydrological model errors are significant. Moreover, using multiple bias correction methods can strengthen the ensemble of future hydro-climatic conditions. These findings have implications for vulnerability–impact–adaptation studies and underscore the importance of rigorous modeling chain design and sensitivity analysis.

Lessons from the calibration and sensitivity analysis of a fish larval transport model

Numerous fish populations show strong year-to-year variations in recruitment. The early life stages play a crucial role in determining recruitment and dispersal patterns. A helpful tool to understand recruitment and dispersal involves simulations with a Lagrangian transport model, which results from the coupling between a hydrodynamic model and an individual-based model. Larval transport models require sound knowledge of the biological processes governing larval dispersal, and they may be highly sensitive to the parameters selected. Various assumptions about larval traits, behaviour and other model parameters can be tested by comparing simulation results with field data to identify the most sensitive parameters and to improve model calibration. This study shows that biological parameterization is more important than inter-annual variability in explaining the year-to-year differences in larval recruitment of common sole in the North Sea and the eastern English Channel. In contrast, year-to-year variability of connectivity leads to higher variability than changes in the biological parameters. The most influential parameters are pelagic larval duration, spawning period and mortality. Calibration over a 12 yr recruitment survey shows that a scenario with low mortality associated with a long larval duration and behaviour involving nycthemeral and tidal migration best reproduces the observations. This research provides insights into factors influencing fish dispersal and recruitment, suggesting a strategy for enhancing the accuracy of models in upcoming studies. The study supports the improvement of larval dispersal modelling by incorporating an easily applicable sensitivity analysis for both calibration and validation. Incorporating sensitivity analyses enhances larval dispersal models, providing performing tools that can contribute to informed fisheries management and understanding of recruitment variability.

A modeling analysis of whole body potassium regulation on a high-potassium diet: proximal tubule and tubuloglomerular feedback effects.

Potassium (K+) is an essential electrolyte that plays a key role in many physiological processes, including mineralcorticoid action, systemic blood-pressure regulation, and hormone secretion and action. Indeed, maintaining K+ balance is critical for normal cell function, as too high or too low K+ levels can have serious and potentially deadly health consequences. K+ homeostasis is achieved by an intricate balance between the intracellular and extracellular fluid as well as balance between K+ intake and excretion. This is achieved via the coordinated actions of regulatory mechanisms such as the gastrointestinal feedforward effect, insulin and aldosterone upregulation of Na+-K+-ATPase uptake, and hormone and electrolyte impacts on renal K+ handling. We recently developed a mathematical model of whole body K+ regulation to unravel the individual impacts of these regulatory mechanisms. In this study, we extend our mathematical model to incorporate recent experimental findings that showed decreased fractional proximal tubule reabsorption under a high-K+ diet. We conducted model simulations and sensitivity analyses to investigate how these renal alterations impact whole body K+ regulation. Model predictions quantify the sensitivity of K+ regulation to various levels of proximal tubule K+ reabsorption adaptation and tubuloglomerular feedback. Our results suggest that the reduced proximal tubule K+ reabsorption under a high-K+ diet could achieve K+ balance in isolation, but the resulting tubuloglomerular feedback reduces filtration rate and thus K+ excretion.NEW & NOTEWORTHY Potassium homeostasis is maintained in the body by a complex system of regulatory mechanisms. This system, when healthy, maintains a small extracellular potassium concentration, despite large fluctuations of dietary potassium. The complexities of the system make this problem well suited for investigation with mathematical modeling. In this study, we extend our mathematical model to consider recent experimental results on renal potassium handling on a high potassium diet and investigate the impacts from a whole body perspective.

Abstract 1939 High sensitivity analysis of tau aggregation models by capillary electrophoresis and Taylor dispersion analysis

Abstract 1939 High sensitivity analysis of tau aggregation models by capillary electrophoresis and Taylor dispersion analysis

Analysing the Impact of Industrial Revolution 4.0 on Traditional Occupations and Community Adaptation Efforts in Bali

This research investigates the impact of Industrial Revolution 4.0 on traditional occupations in Bali, employing a quantitative approach with Structural Equation Modeling and Partial Least Squares (SEM-PLS) analysis. A diverse sample of 250 individuals engaged in agriculture, crafts, and tourism provides insights into the multifaceted nature of Bali's traditional job sectors. The measurement model assessment confirms the reliability and validity of latent variables, including technological adoption, changes in employment patterns, income levels, and community adaptation efforts. The structural model analysis reveals a positive influence of technological adoption on changes in employment patterns and income levels, indicating transformative opportunities within traditional occupations. However, challenges arise in maintaining income equity amidst shifts in employment patterns. The study also highlights the proactive response of communities, with technological adoption positively correlating with effective adaptation efforts. The model fit and sensitivity analysis affirm the robustness of the findings. This research contributes nuanced insights for policymakers and community leaders navigating the intersection of tradition and technological progress in Bali's evolving socio-economic landscape.

Local-scale heterogeneity of soil thermal dynamics and controlling factors in a discontinuous permafrost region

Abstract In permafrost regions, the strong spatial and temporal variability in soil temperature cannot be explained by the weather forcing only. Understanding the local heterogeneity of soil thermal dynamics and their controls is essential to understand how permafrost systems respond to climate change and to develop process-based models or remote sensing products for predicting soil temperature. In this study, we analyzed soil temperature dynamics and their controls in a discontinuous permafrost region on the Seward Peninsula, Alaska. We acquired one-year temperature time series at multiple depths (at 5 or 10 cm intervals up to 85 cm depth) at 45 discrete locations across a 2.3 km2 watershed. We observed a larger spatial variability in winter temperatures than that in summer temperatures at all depths, with the former controlling most of the spatial variability in mean annual temperatures. We also observed a strong correlation between mean annual ground temperature at a depth of 85 cm and mean annual or winter season ground surface temperature across the 45 locations. We demonstrate that soils classified as cold, intermediate, or warm using hierarchical clustering of full-year temperature data closely match their co-located vegetation (graminoid tundra, dwarf shrub tundra, and tall shrub tundra, respectively). We show that the spatial heterogeneity in soil temperature is primarily driven by spatial heterogeneity in snow cover, which induces variable winter insulation and soil thermal diffusivity. These effects further extend to the subsequent summer by causing variable latent heat exchanges. Finally, we discuss the challenges of predicting soil temperatures from snow depth and vegetation height alone by considering the complexity observed in the field data and reproduced in a model sensitivity analysis.

Identification of groundwater nitrate sources in an urban aquifer (Alborz Province, Iran) using a multi-parameter approach.

High concentrations of NO3̄ in water resources are detrimental to both human health and aquatic ecosystems. Identification of NO3̄ sources and biogeochemical processes is a crucial step in managing and controlling NO3̄ pollution. In this study, land use, hydrochemical data, dual stable isotopic ratios and Bayesian Stable Isotope Mixing Models (BSIMM) were integrated to identify NO3̄ sources and estimate their proportional contributions to the contamination of the Karaj Urban Aquifer (Iran). Elevated NO3̄ concentrations indicated a severe NO3̄ pollution, with 39 and 52% of groundwater (GW) samples displaying the concentrations of NO3̄ in exceedance of the World Health Organization (WHO) standard of 50 mg NO3̄ L-1 in the rainy and dry seasons, respectively. Dual stable isotopes inferred that urban sewage is the main NO3̄ source in the Karaj Plain. The diagram of NO3̄/Cl‾ versus Cl‾ confirmed that municipal sewage is the major source of NO3̄. Results also showed that biogeochemical nitrogen dynamics are mainly influenced by nitrification, while denitrification is minimal. The BSIMM model suggested that NO3̄ originated predominantly from urban sewage (78.2%), followed by soil organic nitrogen (12.2%), and chemical fertilizer (9.5%) in the dry season. In the wet season, the relative contributions of urban sewage, soil nitrogen and chemical fertilizer were 87.5, 6.7, and 5.5%, respectively. The sensitivity analysis for the BSIMM modeling indicates that the isotopic signatures of sewage had the major impact on the overall GW NO3̄ source apportionment. The findings provide important insights for local authorities to support effective and sustainable GW resources management in the Karaj Urban Aquifer. It also demonstrates that employing Bayesian models combined with multi-parameters can improve the accuracy of NO3̄ source identification.

An integrative mechanistic model of thymocyte dynamics.

The thymus plays a central role in shaping human immune function. A mechanistic, quantitative description of immune cell dynamics and thymic output under homeostatic conditions and various patho-physiological scenarios are of particular interest in drug development applications, e.g., in the identification of potential therapeutic targets and selection of lead drug candidates against infectious diseases. We here developed an integrative mathematical model of thymocyte dynamics in human. It incorporates mechanistic features of thymocyte homeostasis as well as spatial constraints of the thymus and considerations of age-dependent involution. All model parameter estimates were obtained based on published physiological data of thymocyte dynamics and thymus properties in mouse and human. We performed model sensitivity analyses to reveal potential therapeutic targets through an identification of processes critically affecting thymic function; we further explored differences in thymic function across healthy subjects, multiple sclerosis patients, and patients on fingolimod treatment. We found thymic function to be most impacted by the egress, proliferation, differentiation and death rates of those thymocytes which are most differentiated. Model predictions also showed that the clinically observed decrease in relapse risk with age, in multiple sclerosis patients who would have discontinued fingolimod therapy, can be explained mechanistically by decreased thymic output with age. Moreover, we quantified the effects of fingolimod treatment duration on thymic output. In summary, the proposed model accurately describes, in mechanistic terms, thymic output as a function of age. It may be further used to perform predictive simulations of clinically relevant scenarios which combine specific patho-physiological conditions and pharmacological interventions of interest.

Age and sex-dependent sensitivity analysis of a common carotid artery model

The common carotid artery (CCA) is an accessible and informative site for assessing cardiovascular function which makes it a prime candidate for clinically relevant computational modelling. The interpretation of supplemental information possible through modelling is encumbered by measurement uncertainty and population variability in model parameters. The distribution of model parameters likely depends on the specific sub-population of interest and delineation based on sex, age or health status may correspond to distinct ranges of typical parameter values. To assess this impact in a 1D-CCA-model, we delineated specific sub-populations based on age, sex and health status and carried out uncertainty quantification and sensitivity analysis for each sub-population. We performed a structured literature review to characterize sub-population-specific variabilities for eight model parameters without consideration of health status; variations for a healthy sub-populations were based on previously established references values. The variabilities of diameter and distensibility found in the literature review differed from those previously established in a healthy population. Model diameter change and pulse pressure were most sensitive to variations in distensibility, while pressure was most sensitive to resistance in the Windkessel model for all groups. Uncertainties were lower when variabilities were based on a healthy sub-population; however, the qualitative distribution of sensitivity indices was largely similar between the healthy and general population. Average sensitivity of the pressure waveform showed a moderate dependence on age with decreasing sensitivity to distal resistance and increasing sensitivity to distensibility and diameter. The female population was less sensitive to variations in diameter but more sensitive to distensibility coefficient than the male population. Overall, as hypothesized input variabilities differed between sub-populations and resulted in distinct uncertainties and sensitivities of the 1D-CCA-model outputs, particularly over age for the pressure waveform and between males and females for pulse pressure.

Modelling the Yield and Estimating the Energy Properties of Miscanthus x Giganteus in Different Harvest Periods

This research aims to use artificial neural networks (ANNs) to estimate the yield and energy characteristics of Miscanthus x giganteus (MxG), considering factors such as year of cultivation, location, and harvest time. In the study, which was conducted over three years in two different geographical areas, ANN regression models were used to estimate the lower heating value (LHV) and yield of MxG. The models showed high predictive accuracy, achieving R2 values of 0.85 for LHV and 0.95 for yield, with corresponding RMSEs of 0.13 and 2.22. A significant correlation affecting yield was found between plant height and number of shoots. In addition, a sensitivity analysis of the ANN models showed the influence of both categorical and continuous input variables on the predictions. These results highlight the role of MxG as a sustainable biomass energy source and provide insights for optimizing biomass production, influencing energy policy, and contributing to advances in renewable energy and global energy sustainability efforts.

A two-step Bayesian network-based process sensitivity analysis for complex nitrogen reactive transport modeling

Process-based nitrogen reactive transport models (NRTMs) are vital tools for simulating complex nitrogen reactive transport processes in soils and groundwater to reduce nutrient loss and contamination risk. To develop and improve these models, global sensitivity analysis (GSA) is used to identify key model parameters and processes. Because existing GSA approaches focus mainly on model parameters and not model processes, in this study, we developed a two-step GSA method to improve our understanding of model processes. First, we applied the Morris method to identify less influential parameters. Then, we developed a Bayesian network (BN)-based GSA method to identify the dominant processes for different model predictions. The BN-GSA method accounts for the hierarchical uncertainty in the structure of the model system and flexibly groups uncertain inputs into processes based on their characteristics. The two-step GSA method was applied to a real-world nitrogen reactive transport model with 68 uncertain parameters in three different climate scenarios. The results show that the dominant processes differed for various nitrogen and agricultural outputs; thus, different processes should be emphasized based on specific modeling purposes.

A Fast Algorithm for the Prediction of Distance at Closest Point of Approach in the Maneuvering of Two Ships

Abstract The situation of two ships maneuvering in the vicinity is not so rare, and the significant hydrodynamic interaction effect always poses a threat to navigation safety in these conditions. The assessment of the Distance at Closest Point of Approach (DCPA) is necessary for ship collision avoidance decision-making. In this study, an efficient ship maneuvering model for close-distance maneuvering is developed to predict DCPA rapidly, where the hydrodynamic interaction between ships is accounted for. The Maneuvering Modeling Group (MMG) standard model is used to predict ship motion, and the hydrodynamic interaction effect is estimated in real-time by a code based on the potential flow theory where the variation of free surface is neglected. The ship maneuvering model is validated by the comparison between simulation and experiment in turning and zig-zag maneuvering; furthermore, the validated maneuvering model is used to predict the trajectory under the ship hydrodynamic effect. The characteristic of ship maneuvering under the ship hydrodynamic interaction between two ships is described and illustrated, and the influence of the velocity and lateral distance on the ship trajectory is revealed. A sensitivity analysis of the maneuvering model is performed to determine whether the hydrodynamic interaction effect is necessary to be evaluated exactly for an accurate predicted trajectory and DCPA. The DCPA in different situations is calculated, which represents the ship collision risk.

Modeling and Validation of a Passenger Car Tire Using Finite Element Analysis

This paper focuses on the modeling and analysis of a four-groove passenger car tire, size 235/55R19, using finite element analysis. The Mooney–Rivlin material model is employed to define the hyperelastic behavior of the tire rubber compounds for all solid elements. The tire rim is modeled as a rigid body using aluminum alloy material, and the beads are modeled as beam elements using steel material. The tire model is validated in both static and dynamic domains through several simulations and is compared to published measured data. The tire is validated using footprint and vertical stiffness tests in the static domain. In the static footprint test, a steady-state vertical load is applied, and the tire–road contact area is computed. In the vertical stiffness test, a ramp vertical load is applied, and the tire’s vertical displacement is measured to calculate the tire’s vertical stiffness. In the dynamic domain, the tire is validated using drum-cleat and cornering tests. In the drum-cleat test, a drum with a 2.5 m diameter and a cleat with a 15 mm radius is used to excite the tire structure and obtain the frequency of the vertical and longitudinal first modes of vibration, that is, by applying the fast Fourier transformation (FFT) of the vertical and longitudinal reaction forces at the tire center. In addition to this test, the tire model is pre-steered on a flat surface with a two-degree slip angle and subjected to a steady state linear speed of 10 km/h to predict the cornering force and compute the cornering stiffness. In addition, the effect of tire longitudinal speed on the rolling resistance coefficient is then predicted at zero slip angle using the ISO 28580 rolling resistance test. The findings of this research work provide insights into passenger car tire–road interaction analysis and will be further used to perform tire rubber compound material model sensitivity analysis.

Morphodynamic and modeling insights from global sensitivity analysis of a barrier island evolution model

Recently developed models of coastal barrier morphodynamics include marsh and lagoon processes that have been shown to impact barrier island evolution. To gain additional insights into the simulated barrier-backbarrier system dynamics, this study explores the parameter space of a barrier evolution model using global sensitivity analysis. Influential parameters, their interactions with one another, and regions of sensitivity within the parameter space were identified using Sobol indices and factor mapping techniques for model results through the end of the century. The results of this study highlight an important relationship between initial and critical barrier island geometries and suggest that narrow and low-relief barriers are most vulnerable to be eroded away (width drowning) or overtaken by sea level rise (height drowning), respectively. Width drowning was also strongly associated with other model input parameters such as toe depth, sea level rise rate, and backbarrier critical bed shear stress, which suggests that sub-centennial drowning is dependent on a unique combination of input parameter values and may be averted (or delayed) with a single input parameter change. Barrier dynamics were significantly influenced by the backbarrier marsh platform, which was more impacted by sediment transport parameters such as critical bed shear stress and ocean sediment concentration than maximum annual overwash flux. This suggests that inorganic sediment deposition through tidal inlet dispersion is much more significant to the backbarrier marsh and lagoon system than overwash over sub-centennial timescales and can help to reduce the risk of width drowning.

Sensitivity analysis of an infectious disease model under fuzzy impreciseness

A non-linear dynamic model describing the spread of any infectious disease, based on different categories of individuals: susceptible (S), exposed (E), infected (I), quarantined (Q), recovered (R), dead (D), and vaccinated (V) is presented. The aim of the analysis is to develop a sensitivity technique for fuzzy systems, leveraging the application of fuzzy set theory and its differential calculus. This designed method can significantly generate the sensitivity profile of each measured model output with respect to varying values of input parameters. Additionally, the collective influence of all parameters sensitivities on model is simulated in terms of system sensitivities. Numerical simulations by MATLAB, demonstrate the solution of the fuzzy model and its sensitivities concerning the impreciseness, thereby developing a more generalized and accurate disease model.

Hydrological Simulation Study in Gansu Province of China Based on Flash Flood Analysis

The calibration and validation of hydrological model simulation performance and model applicability evaluation in Gansu Province is the foundation of the application of the flash flood early warning and forecasting platform in Gansu Province. It is difficult to perform the simulation for Gansu Province due to the fact that it covers a wide range, from north to south, with multiple climate types and diverse landforms. The China Flash Flood Hydrological Model (CNFF) was implemented in this study. A total of 11 model clusters and 289 distributed hydrological models were divided based on hydrology, climate, and land-use factors, among others. A spatiotemporally mixed runoff method and the Event-Specific Geomorphological Instantaneous Unit Hydrograph (GIUH) were applied based on large-scale fast parallel computation. To improve model calibration and validation efficiency, the RSA method (Regionalized Sensitivity Analysis) was used for CNFF model parameter sensitivity analysis, which could reduce the number of model parameters that need to be adjusted during the calibration period. Based on the model sensitivity analysis results, the CNFF was established in Gansu Province to simulate flood events in eight representative watersheds. The average NSE, REQ, and ET were 0.76 and 0.73, 9.1% and 12.6%, and 1.2 h and 1.7 h, respectively, in the calibration and validation period. In general, the CNFF model shows a good performance in multiple temporal and spatial scales, thus providing a scientific basis for flash flood early warning and analysis in Gansu Province.