Sensitivity analysis of spatial models using geostatistical simulation

Saint-Geours, Nathalie; Lavergne, Christian; Bailly, Jean-Stéphane; Grelot, Frédéric

doi:10.5242/iamg.2011.0172

Cited by 4 publications

(6 citation statements)

References 10 publications

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“…The Global Spatial Sensitivity Analysis (GSSA) [27,30] relies on Sobol methods [48], which can deal with non-linear and non-monotonic relationships between inputs and output [49][50][51][52]. These methods are based on the functional decomposition of variance (ANOVA) of the model prediction (Y) into partial variances caused due to each model input (X) (either considered singularly or in combination), i.e.,…”

Section: Global Spatial Sensitivity Analysis: Sobol Methodsmentioning

confidence: 99%

“…These variables are then upscaled to various supports (1.6 km, 3.2 km, 6.4 km, and 12.8 km), using two different averaging methodologies, to study the influence of upscaling methods on an RTM performance. Adopting the global spatial sensitivity analysis (GSSA) [27][28][29][30], the sensitivity to soil and vegetation characteristics across scales are evaluated.…”

Section: Introductionmentioning

confidence: 99%

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On the Radiative Transfer Model for Soil Moisture across Space, Time and Hydro-Climates

Neelam

Mohanty

2020

Remote Sensing

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A framework is proposed for understanding the efficacy of the microwave radiative transfer model (RTM) of soil moisture with different support scales, seasonality (time), hydroclimates, and aggregation (scaling) methods. In this paper, the sensitivity of brightness temperature TB (H- and V-polarization) to physical variables (soil moisture, soil texture, surface roughness, surface temperature, and vegetation characteristics) is studied. Our results indicate that the sensitivity of brightness temperature (V- or H-polarization) is determined by the upscaling method and heterogeneity observed in the physical variables. Under higher heterogeneity, the TB sensitivity to vegetation and roughness followed a logarithmic function with an increasing support scale, while an exponential function is followed under lower heterogeneity. Surface temperature always followed an exponential function under all conditions. The sensitivity of TB at H- or V- polarization to soil and vegetation characteristics varied with the spatial scale (extent and support) and the amount of biomass observed. Thus, choosing an H- or V-polarization algorithm for soil moisture retrieval is a tradeoff between support scales, and land surface heterogeneity. For largely undisturbed natural environments such as SGP’97 and SMEX04, the sensitivity of TB to variables remains nearly uniform and is not influenced by extent, support scales, or an upscaling method. On the contrary, for anthropogenically-manipulated environments such as SMEX02 and SMAPVEX12, the sensitivity to variables is highly influenced by the distribution of land surface heterogeneity and upscaling methods.

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Section: Global Spatial Sensitivity Analysis: Sobol Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Radiative Transfer Model for Soil Moisture across Space, Time and Hydro-Climates

Neelam

Mohanty

2020

Remote Sensing

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“…Sample points play an important role in ensuring the accuracy of the surrogate model. When selecting the sample points, we should cover the entire design range as randomly and As a nonoverlapping, spatial, random, and stratified sampling method, Latin hypercube sampling (LHS) [18] divides the design space equally into many uniform probability intervals, conducts sampling in each interval, and then randomly arranges the sampling data of all intervals. The sampling calculation is performed using the following formula:…”

Section: Principle and Constructionmentioning

confidence: 99%

“…In this research, a single-/multiobjective optimization method (termed as RBF-OLHS-NGA/NSGA II method) was constructed for optimizing the design of a leadbismuth reactor core, which involved the RBF surrogate model, orthogonal Latin hypercube sampling (OLHS) [18], and niche/nondominated sorting genetic (NGA/NSGA II) algorithm [19,20]. A design optimization procedure of the lead-bismuth reactor (DOPPLER-R) based on this method was developed, which coupled the reactor Monte Carlo (RMC) [21] code and the steady-state thermal-hydraulic analysis code (STAC) [22,23].…”

Section: Introductionmentioning

confidence: 99%

Radial Basis Function Surrogate Model-Based Optimization of Small Light-Weight Lead-Bismuth Reactor Core

Zijing

Zhao

et al. 2023

International Journal of Energy Research

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Owing to their high miniaturization potential and strong natural circulation capability, lead-bismuth reactors have been widely employed for nuclear energy utilization as well as military and civilian applications. However, there is a need to optimize the design of these reactors. To solve the complex high-dimensional nonlinear single-/multiobjective optimization problem of lead-bismuth reactors, we proposed a single-/multiobjective optimization method (termed as RBF-OLHS-NGA/NSGA II method) for optimizing the reactor core, which is based on the radial basis function (RBF) surrogate model (for prediction), orthogonal Latin hypercube sampling, niche genetic algorithm (for single-objective optimization), and nondominated sorting genetic algorithm (for multiobjective optimization). In view of this approach, the design optimization procedure of the lead-bismuth reactor based on RBF (DOPPLER-R) was developed, which combined the reactor Monte Carlo (RMC) code and the steady-state thermo-hydraulic analysis calculation (STAC) code to sample, predict, and optimize reactor core parameters. Furthermore, the single-/multiobjective optimization approach was verified by considering the core fuel loading and active zone volume of SPALLER-4 as the optimization objectives. The results show that the RBF surrogate model can accurately and rapidly predict the core characteristic parameters of lead-bismuth reactors. When compared to the value calculated using the RMC code, the relative error associated with the predicted effective multiplication factor ( k e f f ) is within ±0.1%. Compared to the unoptimized values, the single-objective optimized fuel loading reduced by 400 kg, multiobjective optimized fuel loading decreased by 455–493 kg (optimization ratio of 78%–84%), and active zone volume reduced by 166362–182888 cm3 (optimization ratio of 72%–79%). These results prove that the proposed optimization method is feasible, exhibits high efficacy, and can provide new technical ideas for single-/multiobjective optimization of multiphysics, multivariable, and multiconstraint lead-bismuth reactors.

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“…In this case, however, all fine‐scale information is lost, thus impeding any analysis of the drivers acting across different scales. An alternative solution is represented by meta‐models, which offer the possibility of reducing model output complexity by establishing a simplified mathematical relationship between the input and output of the system (Simpson et al 2001, Ratto et al 2012, Saint‐Geours 2012, Jia and Taflanidis 2013). Where possible, an elegant way to build meta‐models is the approximation through an analytical model, which is fitted to the large‐scale output and allows for simplification (Grimm and Railsback 2005, Johst 2013).…”

Section: Introductionmentioning

confidence: 99%

Understanding complex spatial dynamics from mechanistic models through spatio‐temporal point processes

et al. 2022

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Landscape heterogeneity affects population dynamics, which determine species persistence, diversity and interactions. These relationships can be accurately represented by advanced spatially-explicit models (SEMs) allowing for high levels of detail and precision. However, such approaches are characterised by high computational complexity, high amount of data and memory requirements and spatio-temporal outputs may be difficult to analyse. A possibility to deal with this complexity is to aggregate outputs over time or space, but then interesting information may be masked and lost, such as local spatio-temporal relationships or patterns. An alternative solution is given by meta-models and meta-analysis, where simplified mathematical relationships are used to structure and summarise the complex transformations from inputs to outputs. Here, we propose an original approach to analyse SEM outputs. By developing a metamodelling approach based on spatio-temporal point processes (STPPs), we characterise spatio-temporal population dynamics and landscape heterogeneity relationships in agricultural contexts. A landscape generator and a spatially-explicit population model simulate hierarchically the pest-predator dynamics of codling moth and ground beetles in apple orchards over heterogeneous agricultural landscapes. Spatio-temporally explicit outputs are simplified to marked point patterns of key events, such as local proliferation or introduction events. Then, we construct and estimate regression equations for multi-type STPPs composed of event occurrence intensity and magnitudes. Results provide local insights into spatio-temporal dynamics of pest-predator systems. We are able to differentiate the contributions of different driver categories (i.e. spatiotemporal, spatial, population dynamics). We highlight changes in the effects on occurrence intensity and magnitude when considering drivers at global or local scale. This approach leads to novel findings in agroecology where, for example, we show that the organisation of cultivated patches and semi-natural elements play different roles for pest regulation depending on the scale considered. It aids to formulate guidelines for biological control strategies at global and local scale.

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Sensitivity analysis of spatial models using geostatistical simulation

Cited by 4 publications

References 10 publications

On the Radiative Transfer Model for Soil Moisture across Space, Time and Hydro-Climates

On the Radiative Transfer Model for Soil Moisture across Space, Time and Hydro-Climates

Radial Basis Function Surrogate Model-Based Optimization of Small Light-Weight Lead-Bismuth Reactor Core

Understanding complex spatial dynamics from mechanistic models through spatio‐temporal point processes

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