Sensitivity Analysis Of Model Research Articles

ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool

Abstract. The accurate calibration of parameters in atmospheric and Earth system models is crucial for improving their performance but remains a challenge due to their inherent complexity, which is reflected in input–output relationships often characterised by multiple interactions between the parameters, thus hindering the use of simple sensitivity analysis methods. This paper introduces the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a new tool designed with the aim of providing a simple and flexible framework to estimate the sensitivity and importance of parameters in complex numerical weather prediction models. This tool leverages the strengths of multiple regression-based and probabilistic machine learning methods, including LASSO (see the list of abbreviations in Appendix B), support vector machine, classification and regression trees, random forest, extreme gradient boosting, Gaussian process regression, and Bayesian ridge regression. These regression algorithms are used to construct computationally inexpensive surrogate models to effectively predict the impact of input parameter variations on model output, thereby significantly reducing the computational burden of running high-fidelity models for sensitivity analysis. Moreover, the multi-method approach allows for a comparative analysis of the results. Through a detailed case study with the Weather Research and Forecasting (WRF) model coupled with the Noah-MP land surface model, ML-AMPSIT is demonstrated to efficiently predict the effects of varying the values of Noah-MP model parameters with a relatively small number of model runs by simulating a sea breeze circulation over an idealised flat domain. This paper points out how ML-AMPSIT can be an efficient tool for performing sensitivity and importance analysis for complex models, guiding the user through the different steps and allowing for a simplification and automatisation of the process.

Construction of geriatric hypoalbuminemia predicting model for hypoalbuminemia patients with and without pneumonia and explainability analysis.

Pneumonia portrays a critical health concern in geriatrics. Geriatric pneumonia can lead to changes on other complications, in which hypoalbuminemia is a common complication. However, few studies have looked at the impact of pneumonia on the course of hypoalbuminemia and predicting. This study aims to predicting hypoalbuminemia in geriatric pneumonia and non-pneumonia patients and exploring the clinical difference between the two groups. This retrospective study enrolled 42 pneumonia patients group and 76 non-pneumonia patients group. The indicators difference of different groups were analyzed, then a mutual information-grey relational coefficient gradual fusion model was constructed to predict hypoalbuminemia in the future by the indicators of vital signs, N-Terminal Pro-Brain Natriuretic Peptide, blood routine examination and urine routine examination at admission. Through the sensitivity analysis of model, we analysed the important of four examines in patients with and without pneumonia. The predicted accuracy of our gradual fusion model was 0.954, which improve the prediction accuracy by nearly 17.6% compared with the classical machine learning method. The AUC of gradual fusion model was 0.96 and 0.9 in hypoalbuminemia patients with and without pneumonia. The sensitivity analysis of gradual fusion model showed blood routine examine was most important to predict hypoalbuminemia in patients with pneumonia, while urine routine examine was most important to predict hypoalbuminemia in non-pneumonia patients. The changes in the blood of patients with hypoalbuminemia combined with pneumonia were more significant than that of patients with hypoalbuminemia alone, which was characterized by abnormal excretion due to low protein. We suggested doctors should pay more attention to blood routine results when preventing hypoalbuminemia in patients with pneumonia and pay more attention to urine routine examine results when preventing hypoalbuminemia in patients without pneumonia.

Study of bifurcations, chaotic structures with sensitivity analysis and novel soliton solutions of non-linear dynamical model

The current work amalgamates the fascinating exploration of the Radhakrishnan–Kundu–Lakshmanan equation, which is a sophisticated mathematical framework in physics and finds applications in elucidating diverse physical phenomena. By leveraging the two recently developed computational approaches, namely the new extended direct algebraic method and the modified Sardar sub-equation method, we rigorously assess the novel soliton solutions including dark, bright-dark, dark-bright, periodic, singular, rational and mixed trigonometric forms. Furthermore, we also segregated W-shape, M-shape, bell shape, exponential, as well as hyperbolic soliton, which are not documented in the literature. We validate the stability and accuracy of extracted soliton wave solutions using the Hamiltonian property. Additionally, the Galilean transformation is applied and numerous standard types of results, including bifurcations, chaotic flows, and sensitivity analysis are presented. The obtained results are tested both numerically and with illuminating physical interpretations, which shows a better demonstration of the intricate dynamics of these models.

Numerical Bifurcations and Sensitivity Analysis of an SIVPC Cervical Cancer Model

Numerical Bifurcations and Sensitivity Analysis of an SIVPC Cervical Cancer Model

Sensitivity analysis of closed-loop one-chamber and four-chamber models with baroreflex.

The baroreflex is one of the most important control mechanisms in the human cardiovascular system. This work utilises a closed-loop in silico model of baroreflex regulation, coupled to pulsatile mechanical models with (i) one heart chamber and 36-parameters and (ii) four chambers and 51 parameters. We perform the first global sensitivity analysis of these closed-loop systems which considers both cardiovascular and baroreflex parameters, and compare the models with their respective unregulated equivalents. Results show the reduced influence of regulated parameters compared to unregulated equivalents and that, in the physiological resting state, model outputs (pressures, heart rate, cardiac output etc.) are most sensitive to parasympathetic arc parameters. This work provides insight into the effects of regulation and model input parameter influence on clinical metrics, and constitutes a first step to understanding the role of regulation in models for personalised healthcare.

SYSTEM VARIABLE REDUCTION AND GLOBAL SENSITIVITY ANALYSIS FOR A COMPLEX MODEL OF CANCER CELL DIFFERENTIATION

Model reduction aims to simplify complex models by decreasing the number of equations, variables, or parameters while preserving key characteristics. This approach enhances accessibility, comprehensibility, and computational efficiency, enabling a more focused analysis of relevant variables. In this study, we describe the reduction process of a population model that incorporates cancer cell differentiation and its interaction with the immune system, maintaining the fundamental dynamics and evolution of the original model. This led to a substantial reduction in variables and parameters, creating a more efficient model suitable for computational simulations, mathematical analysis, and quantitative understanding of population dynamics. Additionally, we performed a global sensitivity analysis of model parameters using the Sobol and eFast methods, revealing insights into differences and similarities in results from a biological perspective. Our findings emphasize the critical importance of understanding and controlling parameters related to the reproduction and death rates of differentiated cancer cells, as small variations in these parameters can have significant effects on model outcomes. This underscores the importance of thoroughly understanding these essential biological variables and processes in cancer treatment, as they have a significant impact on model outcomes and, consequently, on the development of more effective therapies.

Sensitivity analysis of WRF model to physical parameterization for the characterization of coastal and offshore wind patterns in Caspian Sea region

Sensitivity analysis of WRF model to physical parameterization for the characterization of coastal and offshore wind patterns in Caspian Sea region

Bifurcation and sensitivity analysis along with the numerical simulations of susceptible breaking-out quarantine treatment recovered computer model

Internet worms have received a lot of attention because of their massive hazards to the internet. As the internet worms propagate quickly, therefore, the automatic control on the internet is required. In this paper, we present a new epidemic model called susceptible-breaking out-quarantine-treatment-recovered (SBQTR) model which integrates dynamic quarantine and treatment approaches to control the spread of internet worms. By using the SBQTR model, we can calculate the basic reproduction number [Formula: see text], which determines the existence of worms. The model has two equilibrium states: virus-free equilibrium (VFE) and viral equilibrium (VE). The stability analysis of both the equilibrium points is carried out and impact of treatment is examined under various parametric values. The sensitivity analysis of model is carried out to determine the impact of model parameters on virus transmission. Furthermore, we established nonstandard finite difference (NSFD) schemes and Runge–Kutta algorithm of order 4 (RK-4) for SBQTR model. The stated NSFD technique retains all of the significant properties of system (1), whereas RK-4 mechanism gives negative solutions and doesn’t converge to the equilibrium points of system (1). Numerical results together with comparison for various values of step size ([Formula: see text]) are available. Bifurcation value of infection coefficient is also investigated. It is shown that an appropriate treatment is essential to control the spread of worms in computer network.

RANK REVERSAL IN ECONOMIC DECISION MAKING

Economic decision making is often based on a sequence of ranking alternatives to ensure reasonable decisions. Cases of rank reversals, in which the order of preference of choices is altered by the introduction or removal of options, present important issues. This study examines the occurrence of degree of change characterized by uncertainty in economic parameters in environment Z. The study examines the causes of rate fluctuations and their implications for economic theory and practice using advanced decision-making models and simulations The findings show that rank change in environment Z is not only a theoretical irregularity, but also a real concern that can lead to bad economic outcomes. This study presents a relatively low-complexity, theoretically driven, and comprehensive method to decision-making under bimodal uncertainty, as represented by Z-numbers. The suggested technique for making decisions is based on the consistency-driven preferences creation method, and the eigenvector determination method of Z-number valued matrixes. The study concludes with recommendations to avoid rate-shifting in economic decision-making, emphasizing the need for vigorous modeling approaches and sensitivity analysis. The recommended method's correctness and viability are demonstrated with an numerical example using Z-lab tool created for calculation of Z-numbers and sets. Keywords: Z-number, economic decision making, consistent preferences.

DARWEN: Data-driven Algorithm for Reduction of Wide Exoplanetary Networks

Context. Exoplanet atmospheric modeling is advancing toward complex coupled circulation-chemistry models, from chemically diverse 1D models to 3D global circulation models (GCMs). These models are crucial for interpreting observations from facilities like JWST and ELT and understanding exoplanet atmospheres. However, maintaining chemical diversity in 1D models and especially in GCMs is computationally expensive, limiting their complexity. Optimizing the number of reactions and species in the simulated atmosphere can address this tradeoff, but there is a lack of transparent and efficient methods for this optimization in the current exoplanet literature. Aims. We aim to develop a systematic approach for reducing chemical networks in exoplanetary atmospheres, balancing accuracy and computational efficiency. Our method is data-driven, meaning we do not manually add reactions or species. Instead, we test possible reduced chemical networks and select the optimal one based on metrics for accuracy and computational efficiency. Our approach can optimize a network for similar planets simultaneously, can assign weights to prioritize either accuracy or efficiency, and is applicable in the presence of photochemistry. Methods. We propose an approach based on a sensitivity analysis of a typical 1D chemical kinetics model. Principal component analysis was applied to the obtained sensitivities. To achieve a fast and reliable reduction of chemical networks, we utilized a genetic algorithm (GA), a machine-learning optimization method that mimics natural selection to find solutions by evolving a population of candidate solutions. Results. We present three distinct schemes tailored for different priorities: accuracy, computational efficiency, and adaptability to photochemistry. These schemes demonstrate improved performance and reduced computational costs. Our work represents the first reduction of a chemical network with photochemistry in exoplanet research. Conclusions. Our GA-based method offers a versatile and efficient approach to reduce chemical networks in exoplanetary atmospheres, enhancing both accuracy and computational efficiency.

Modeling and experimentally-driven sensitivity analysis of wake-induced power loss in offshore wind farms: Insights from Block Island Wind Farm

Modeling and experimentally-driven sensitivity analysis of wake-induced power loss in offshore wind farms: Insights from Block Island Wind Farm

Determination of the significance of hydraulic fracturing success factors for shale reservoir

Abstract Shale gas is a shale rock that has low permeability. The right way to explore it is to do hydraulic fracturing to increase well productivity by injecting fluid into the fracture using high pressure. The statistical method used to establish a relationship between one independent variable X or more with a response variable Y uses linear regression analysis. The method used in this research is simulation research, using a CMG GEM simulator for reservoir simulation modeling and running sensitivity analysis using CMOST for as much as 80 data, then performing multiple linear regression analysis with SPSS (Statistical Product for Service Solutions). This study aims to determine the factor parameters that are significant to the success of hydraulic fracturing in shale gas. Parameters used for sensitivity analysis were significant factors for successful hydraulic fracturing: fracture half-length, matrix porosity, young elastic modulus, natural fracture porosity, Poisson ratio, and Langmuir pressure. The results of the T-test, which were carried out partially sequentially, showed that the variable X1 (fracture half-length) effect with a significant value of 0.985 > 0.05, variable X3 (Young’s modulus of elasticity) significant value of 0.734, variable X4 (natural fracture porosity), dan variable X6 (pressure langmuir) significant value 0.088. In addition to the results of the F hypothesis, the calculated F value simultaneously (together) is 11.231. While the R-value Square (R2) is 0.480 if it is a percentage, the result is 48%. It can be concluded that the six independent variables affect the dependent variable by 48%. The remaining percentage, about 52%, is influenced by other independent variables, which are not examined in this study.

A high-resolution 3D sediment-dynamics model of a morphologically complex mesotidal estuarine river system (New Zealand): insights from model calibration and validation

Land use change and the associated change in sediment runoff to the receiving environment is a topic of concern for estuarine and coastal governing agencies around the world. Fine sediments can enter the receiving environment and have a plethora of potential negative impacts (e.g., ecological, and recreational) and present no real opportunities or positive impacts. In the present study, a Delft3D numerical model is presented. The model has been calibrated considering water levels, currents, temperature, salinity, and Suspended Sediment Concentrations (SSC). Delft3D-Wave was also coupled to the hydrodynamic model to investigate the role of wave related fine sediment resuspension due to wave bed shear stresses. The extent of this model covered the Wairoa River and estuary, just south of Auckland, New Zealand. The motivation for this study was to produce a method that could link the upstream SSC contribution to the subsequent distribution in the receiving environment. From there it can readily be used as input to a regional scale model to investigate and understand the fate of sediments in the Hauraki Strait. As part of an ongoing research programme, the present study formed part of an extensive measurement campaign. Measurement instruments and results are presented alongside numerical model sensitivity analyses. The model performance is discussed, and the physical dynamics are described via extensive tables and summarising plots.

Validating ex210Pb sediment dating methods applied to a large anthropogenically-impacted river basin

Sediment dynamics in five sites within the Mobile River Basin, Alabama, impacted by (i) dam-and-lock use, (ii) urbanization, (iii) industrial/mining practices, (iv) flooding, and (v) storm surge events were evaluated to understand better anthropogenic impacts on regional sediment budgets. Three widely used sediment dating models based on excess 210Pb (ex210Pb) (i.e., Constant Rate of Supply, CRS, Constant Initial Concentration, CIC, and Advective Dispersion Equation, ADE) and two recently developed ones (i.e., Porosity Variation, PV and Porosity Variation Without Diffusion, PVWD) were tested to determine their applicability to these anthropogenically altered lacustrine and coastal areas. To verify results from ex210Pb models, we used conventional time markers, including regional hydrograph records and historical aerial images. We found that the traditional time-marker 137Cs is no longer helpful due to its current low inventories in the sediments. Drainage-to-lake area ratios were used to determine relative runoff and atmospheric radionuclide contributions. Statistical analysis of physical properties such as porosity, bulk and dry density, water content, and sediment geochemical compositions were utilized to support sediment transport and site development hypotheses. When constructing the sediment history at each site using these proxies, we found that the conventional CIC and ADE models produced unreliable ages because of violation of requirements for exponentially decreasing porosity and ex210Pb activity with depth. We found that two new models, PV and PVWD, that account for heterogeneous porosity, produced more reliable sediment ages. These two models produced ages with lower uncertainties than the CRS model, outperforming the other conventional models tested. We conclude that the PV and PVWD models are more appropriate for environments experiencing erosional and abrupt depositional events, which in our study resulted from dam construction and storm-surge events. Model sensitivity analysis showed that decreasing average particle density produces younger sediment ages by the PV and PVWD models. Higher ex210Pb activity analytical uncertainty resulted in lower sedimentation rates and higher estimated ages by all five models.

Performance and sensitivity analysis of the Morpho2DH model in extreme events in southern Brazil

Debris flows are a mixture of water and sediment with a continuous fluid behavior driven mainly by gravity. Computational modeling can be used to predict areas susceptible to these phenomena. When using computational modeling, sensitivity analysis is a crucial step to evaluate and interpret the model results. Thus, the objective of this study was to evaluate the performance of the Morpho2DH debris flow model, perform a sensitivity analysis of its input parameters, recommend application criteria, and increase the use of such a model in South America and Brazil. The model was applied in two representative areas with occurrences of debris flows in southern Brazil. The methodology consisted of two steps: (i) model calibration; (ii) one-at-a-time (OAT) sensitivity analysis (SA) for selected input parameters. The sensitivities of input parameters were evaluated for total area affected, distance traveled, mean velocity, and deposited volume. The results of the SA demonstrate that the most sensitive parameters were maximum bed erosion depth, resistance coefficient, minimum flow depth, and mean particle diameter. Meanwhile, static bed sediment concentration, vegetation, and internal friction angle showed moderate sensitivity. Parameters such as liquid density, sediment concentration, and sediment density showed the lowest sensitivities. Finally, it is recommended to use the model for situations where input volume and terrain data (mainly the erosion thicknesses or soil depth) are well-defined. Additionally, the computational cost of this model should be considered, concerning the magnitude of the event to be simulated.

Dynamic Time Warping as Elementary Effects Metric for Morris-Based Global Sensitivity Analysis of High-Dimension Dynamical Models

This work focused on demonstrating the use of dynamic time warping (DTW) as a metric for the elementary effects computation in Morris-based global sensitivity analysis (GSA) of model parameters in multivariate dynamical systems. One of the challenges of GSA on multivariate time-dependent dynamics is the modeling of parameter perturbation effects propagated to all model outputs while capturing time-dependent patterns. The study establishes and demonstrates the use of DTW as a metric of elementary effects across the time domain and the multivariate output domain, which are all aggregated together via the DTW cost function into a single metric value. Unlike the commonly studied coefficient-based functional approximation and covariance decomposition methods, this new DTW-based Morris GSA algorithm implements curve alignment via dynamic programing for cost computation in every parameter perturbation trajectory, which captures the essence of “elementary effect” in the original Morris formulation. This new algorithm eliminates approximations and assumptions about the model outputs while achieving the objective of capturing perturbations across time and the array of model outputs. The technique was demonstrated using an ordinary differential equation (ODE) system of mixed-order adsorption kinetics, Monod-type microbial kinetics, and the Lorenz attractor for chaotic solutions. DTW as a Morris-based GSA metric enables the modeling of parameter sensitivity effects on the entire array of model output variables evolving in the time domain, resulting in parameter rankings attributed to the entire model dynamics.

Uncertainty Analysis in Ecological Risk Assessment: Sensitivity Analysis of Different Exposure Models

Ecological risk assessments are used for decision making regarding releasing new products to market as well as remedial action required to address contamination at legacy sites. Uncertainty analysis is considered a key part of the process (USEPA 1997) but assessments seldom address uncertainty quantitatively. Key sources of uncertainty include: Sampling and analytical variability (soil/ sediment matrix, lab error), Choice of “Indicator” Species as Target Receptors for Different Exposure Pathways, Sample size. Bioaccumulation and Food Chain Modeling involve assumptions and uncertainty including: Linear assumption of bioaccumulation and selection of bioaccumulation factors; Other model inputs such as: Home range size, Dietary percentages, Body Weights, Ingestion Rate; Literature data on effects, Use of No Observed Adverse Effects Level (NOAEL) or Lowest Observed Adverse Effects Level (LOAEL) as a decision point. Toxicity testing where employed brings its own set of assumptions and interpretation that are subject to uncertainty. These include: Use of single organism, Correlation (or lack thereof) with Contaminants of Concern, Contaminant mixtures, and Reference area selection. Keywords: Ecotoxicology, Risk Assessment, Ecology

Development of building benchmarking index for improving gross-floor-area-based energy use intensity

Energy benchmarking is essential for evaluating building performance and developing energy reduction strategies, typically using floor-area-based energy use intensity (EUI). However, this method has limitations. Studies show that metrics such as site energy, source energy, and CO2 emissions can produce varying benchmark results for the same building, raising concerns about reliability. As carbon reduction in buildings becomes increasingly important, there is a need for comprehensive indicators that assess both energy and carbon efficiency. This study introduces the Building Performance Index (BPI), that integrates energy and carbon emission efficiencies. Using a database of all buildings in Seoul, South Korea, we compared five BPI variants: conventional floor-area-based BPI, floor-area-based BPI adjusted for floor area ratio, volume BPI based on floor area, volume BPI adjusted for floor area ratio, and volume BPI based on building footprint. These were evaluated based on six criteria, including model simplicity, sensitivity analysis, data reliability, and indicator stability. While the relative importance of these criteria was not prioritized, this study proposes a shift from traditional two-dimensional metrics to more comprehensive three-dimensional volume-based metrics, offering practical guidance for decision-makers in selecting more reliable benchmarking methods.

Sensitivity and uncertainty analysis of a surface runoff model using ensemble of artificial rainfall experiments

Abstract Surface runoff models are essential for designing water and soil protection measures. However, they often exhibit uncertainty in both parameterization and results. Typically, uncertainty is evaluated by comparing model realizations with measured data. However, this approach is constrained by limited data availability, preventing comprehensive uncertainty assessment. To overcome this limitation, we employed the generalized likelihood uncertainty estimation (GLUE) methodology to conduct sensitivity and uncertainty analyses on a series of surface runoff models. These models were based on an ensemble of artificial rainfall experiments comprising 77 scenarios with similar settings. We utilized the rainfall-runoff-erosion model SMODERP2D to simulate the experiments and employed Differential Evolution, a heuristic optimization method, to generate sets of behavioural models for each experiment. Additionally, we evaluated the sensitivity and uncertainty with respect to two variables; water level and surface runoff. Our results indicate similar sensitivity of water level and surface runoff to most parameters, with a generally high equifinality. The ensemble of models revealed high uncertainty in bare soil models, especially under dry initial soil water conditions where the lag time for runoff onset was the largest (e.g. runoff coefficient ranged between 0–0.8). Conversely, models with wet initial soil water conditions exhibited lower uncertainty compared to those with dry initial soil water content (e.g. runoff coefficient ranged between 0.6 – 1). Models with crop cover showed a multimodal distribution in water flow and volume, possibly due to variations in crop type and growth stages. Therefore, distinguishing these crop properties could reduce uncertainty. Utilizing an ensemble of models for sensitivity and uncertainty analysis demonstrated its potential in identifying sources of uncertainty, thereby enhancing the robustness and generalizability of such analyses.

Ferroptosis-related prognostic model of mantle cell lymphoma.

Mantle cell lymphoma (MCL) is a B-cell non-Hodgkin's lymphoma. Ferroptosis, an iron-dependent programmed cell death, is closely related to cancer prognosis. In this study, we established a model of ferroptosis related genes for prognostic evaluation of patients with MCL. Using the single-cell RNA sequencing datasets GSE184031 and mRNA sequencing data GSE32018 from the Gene Expression Omnibus, we identified 139 ferroptosis-related genes in MCL. Next a prognostic model was constructed by Cox regression and Least absolute selection and shrinkage Operator regression analysis. Finally, we used CIBERSORT to analyze the immune microenvironment and the "oncoPredict" package to predict potential drugs. In our model, the prognosis of MCL patients was assessed by risk scoring using 7 genes ANXA1, IL1B, YBX1, CCND1, MS4A1, MFHAS1, and RILPL2. The patients were divided into high-risk and low-risk groups based on our model, and the high-risk patients had inferior overall survival. Finally, according to our model and computational drug sensitivity analysis, four small molecule compounds, BMS-754807, SB216763, Doramapimod, and Trametinib, were identified as potential therapeutic agents for patients with MCL. In summary, we provide a prognostic model with ferroptosis-related gene signature for MCL. This study provides a prognostic model with ferroptosis-related gene signature for MCL. The results show that the model helps predict prognosis in MCL.