Modeling partial cross-protection for managing cassava mosaic disease: a vector–host framework and sensitivity analysis

Conversion and interconnection of multiple energies have become the main form of energy development in China. This paper analyzes the evolution of energy internet and proposes its basic framework based on summarized typical features. As we all know, the mathematical model in energy internet plays increasingly key role in arranging and optimizing resources as well as predicting work. In real applications, we find many complicated problems of electrical model, such as high-dimensional, multiple peaks, non-linear, inconsistent, and nonconvex. In order to solve them, this paper combines model simulation with Global Uncertainty and Sensitivity Analysis to analyse and predict the scientific problem from a probabilistic viewpoint. Global Sensitivity Analysis aims to find out how changes of multiple parameters affecting working result of electrical model, and to analyze how interactions among parameters influencing model results. While modeling each type of electrical model, Global Sensitivity Analysis are used less, this paper also lists various sensitivity analysis algorithm, and describes kinds of methods of both local and global sensitivity analysis. The research will provide a complete method data base of sensitivity analysis for each model development in electrical system. Furthermore, the combination of uncertainty and sensitivity analysis at home and abroad is beneficial to figure out the difficulties and focuses of connections among researches on sensitivity analysis and its common features and the spatially explicit landscape modeling.

Quasi-Monte Carlo based global uncertainty and sensitivity analysis in modeling free product migration and recovery from petroleum-contaminated aquifers

Part II of this review paper highlights the salient features of the most popular statistical methods currently used for local and global sensitivity and uncertainty analysis of both large-scale computational models and indirect experimental measurements. These statistical procedures represent sampling-based methods (random sampling, stratified importance sampling, and Latin Hypercube sampling), first- and second-order reliability algorithms (FORM and SORM, respectively), variance-based methods (correlation ratio-based methods, the Fourier Amplitude Sensitivity Test, and the Sobol Method), and screening design methods (classical one-at-a-time experiments, global one-at-a-time design methods, systematic fractional replicate designs, and sequential bifurcation designs). It is emphasized that all statistical uncertainty and sensitivity analysis procedures first commence with the “uncertainty analysis” stage and only subsequently proceed to the “sensitivity analysis” stage; this path is the exact reverse of the conceptual path underlying the methods of deterministic sensitivity and uncertainty analysis where the sensitivities are determined prior to using them for uncertainty analysis.By comparison to deterministic methods, statistical methods for uncertainty and sensitivity analysis are relatively easier to develop and use but cannot yield exact values of the local sensitivities. Furthermore, current statistical methods have two major inherent drawbacks as follows:1. Since many thousands of simulations are needed to obtain reliable results, statistical methods are at best expensive (for small systems) or, at worst, impracticable (e.g., for large time-dependent systems).2. Since the response sensitivities and parameter uncertainties are inherently and inseparably amalgamated in the results produced by these methods, improvements in parameter uncertainties cannot be directly propagated to improve response uncertainties; rather, the entire set of simulations and statistical postprocessing must be repeated anew. In particular, a “fool-proof” statistical method for correctly analyzing models involving highly correlated parameters does not seem to exist currently, so that particular care must be used when interpreting regression results for such models.By addressing computational issues and particularly challenging open problems and knowledge gaps, this review paper aims at providing a comprehensive basis for further advancements and innovations in the field of sensitivity and uncertainty analysis.

Evaluating ecological resilience with global sensitivity and uncertainty analysis

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low-quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision-making framework will result in better-informed, more robust decisions.

Sensitivity and uncertainty analysis are important tools in the modelling process: they assign confidence to model results, can aid in focusing monitoring and preservation efforts, and can be used in model simplification. A weakness of global sensitivity and uncertainty analysis methodologies is the often subjective definition of prior parameter probability distributions, especially in data-poor areas. We apply Monte Carlo filtering in conjunction with quantitative variance-based global sensitivity and uncertainty analysis techniques to address this weakness and define parameter probability distributions in the absence of measured data. This general methodology is applied to a reservoir model of the Okavango Delta, Botswana. In addition to providing a methodology for setting prior parameter distributions, results show that the use of Monte Carlo filtering reduces model uncertainty and produces simulations that better represent the calibrated ranges. Thus, Monte Carlo filtering increases the accuracy and precision of parametric model uncertainty. Results also show that the most important parameters in our model are the volume thresholds, the reservoir area/volume coefficient, floodplain porosity, and the island extinction coefficient. The reservoir representing the central part of the wetland, where flood waters separate into several independent distributaries, is a keystone area within the model. These results identify critical areas and parameters for monitoring and managing, refine and reduce input/output uncertainty, and present a transferable methodology for developing parameter probability distribution functions, especially when using empirical models in data-scarce areas. Keywords : sensitivity analysis, uncertainty analysis, Monte Carlo filtering, reservoir model, Okavango Delta

Crop model parameters usually vary with diverse field management and environment conditions, which hinder the model calibration. Sensitivity analysis (SA) and uncertainty analysis (UA) for model parameters and outputs are helpful for calibrating crop model under diverse conditions. A global SA and UA were used to determine the maize parameters sensitivity and outputs uncertainty of the AquaCrop model under different irrigation and fertilizer management conditions, i.e. none, slight, and moderate stress pertaining to water and fertility. The results indicated that the sensitive parameters to maximum above ground biomass (AGB) and yield differed from those to canopy cover development and AGB production (time-response outputs of the AquaCrop). The sensitive parameters should be preferentially calibrated given their strong effects on all type of model outputs, besides, the sensitive parameters to the model time-response outputs were somewhat different from those to the model non-time-response outputs. The interaction effects among parameters on the model outputs should receive more attention in the model calibration. The SA and UA results between diverse management were clearly different, and these differences between different fertility stress levels were larger. Fertility stress is a more influential factor on parameters sensitivity and outputs uncertainty than water stress in the AquaCrop model.

An agent-based approach to global uncertainty and sensitivity analysis

Simulating the fate of Florida Snowy Plovers with sea-level rise: Exploring research and management priorities with a global uncertainty and sensitivity analysis perspective

Global uncertainty and sensitivity analysis of a spatially distributed ecological model

In the last decade, first-principles-based microkinetic modeling has been developed into an important tool for a mechanistic understanding of heterogeneous catalysis. A commonly known, but hitherto barely analyzed issue in this kind of modeling is the presence of sizable errors from the use of approximate Density Functional Theory (DFT). We here address the propagation of these errors to the catalytic turnover frequency (TOF) by global sensitivity and uncertainty analysis. Both analyses require the numerical quadrature of high-dimensional integrals. To achieve this efficiently, we utilize and extend an adaptive sparse grid approach and exploit the confinement of the strongly non-linear behavior of the TOF to local regions of the parameter space. We demonstrate the methodology on a model of the oxygen evolution reaction at the Co3O4 (110)-A surface, using a maximum entropy error model that imposes nothing but reasonable bounds on the errors. For this setting, the DFT errors lead to an absolute uncertainty of several orders of magnitude in the TOF. We nevertheless find that it is still possible to draw conclusions from such uncertain models about the atomistic aspects controlling the reactivity. A comparison with derivative-based local sensitivity analysis instead reveals that this more established approach provides incomplete information. Since the adaptive sparse grids allow for the evaluation of the integrals with only a modest number of function evaluations, this approach opens the way for a global sensitivity analysis of more complex models, for instance, models based on kinetic Monte Carlo simulations.

Global Uncertainty and Sensitivity Analysis for Robust Design of a Rotary Kiln Process

We shall briefly review recent progress in Global Quantitative Uncertainty and Sensitivity Analysis (UA/SA) techniques, relating these to multidimensional global calibration approaches of the “Monte Carlo filtering” type. Global quantitative techniques for Sensitivity Analysis (SA), that are based on the decomposition of the variance of the target model output, have received a considerable boost in recent years, due both to more efficient computational strategies and to a widening of their range of applications. Monte Carlo Filtering, and the GLUE (Generalised Likelihood Uncertainty Estimate) approach that derives from it are also promising tools to use in the presence of structural model uncertainty, as in the case of petroleum engineering that was the focus of the ECMI2002 mini-symposium “Advanced Mathematical Tools for Petroleum System Modelling”.

Water stress is one of the crucial factors that determine crop yield losses. The Agricultural Reference Index for Drought (ARID) is a generic plant water stress index that estimates the level of water stress as the ratio of actual evapotranspiration to reference evapotranspiration, on a daily time step, considering a hypothetical grass reference crop. The objective of this study was to determine the effect of soil inter‐ and intraclass variability, plant characteristics, and climate locations on ARID output using global sensitivity and uncertainty analysis. Root zone depth, maximum uptake factor, the wilting point (θwp), field capacity (θfc), drainage coefficient, and runoff curve number were the ARID model parameters, while inputs consisted of rainfall and reference evapotranspiration. Global sensitivity analysis was performed for five locations in the southeastern United States and four soil types, sandy loam, silt loam, sandy clay loam, and silty clay, using three methods, the method of Sobol’, partial correlation coefficients, and partial rank correlation coefficients. Sensitivity analyses results indicated the monotonic and linear nature of ARID. The effect of location on the results of this study was minimal. Root zone depth was found to be the most influential model parameter, followed by soil hydraulic properties (i.e., θfc and θwp). Uncertainty in ARID outputs varied from soil to soil (intersoil variability), with soils having similar sand content showing similar results. Considerable uncertainty was seen in the sandier soils, indicating the need for accurate estimation of soil hydraulic properties in such soils.

ABSTRACTGeographic information system (GIS)-based multi-criteria decision analysis (MCDA) is commonly used to solve a range of complex spatial problems. We have incorporated an analytical network process (ANP) as a central part of MCDA and used this approach for land subsidence susceptibility mapping (LSSM). We used an ANP approach in combination with the concepts of global sensitivity and uncertainty analyses, in order to minimize any associated errors. This approach was tested for a LSSM application in north-western Iran, using a methodology that consisted of three distinct phases. An ANP approach was used in the first phase to derive criteria weightings by analysing relevant criteria, which included topographic, hydrologic, climatologic, geological, and anthropological indicators. The second phase involved evaluating the uncertainty and sensitivity of areas susceptible to subsidence as a function of the derived weightings, using Monte Carlo simulations. The third phase then used a land subsidence inventory database to validate the results and to measure any improvement in accuracy following sensitivity analysis. Results indicated a 6% improvement in the accuracy of the ANP method as a result of using an integrated global sensitivity analysis.