Higher-order sensitivity analysis of a final repository model with discontinuous behaviour using the RS-HDMR meta-modeling approach

2019 ◽  
Vol 187 ◽  
pp. 149-158 ◽  
Author(s):  
Sabine M. Spiessl ◽  
Sergei Kucherenko ◽  
Dirk-A. Becker ◽  
Oluyemi Zaccheus
Keyword(s):  
Sensitivity Analysis ◽  
Higher Order ◽  
Modeling Approach ◽  
Meta Modeling

Comprehensive global sensitivity analysis of a repository model using different types of transformations and metamodeling techniques

2021 ◽  
Author(s):  
Sabine M. Spiessl ◽  
Dirk-A. Becker ◽  
Sergei Kucherenko
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Higher Order ◽  
Model Parameters ◽  
High Dimensional Model Representation ◽  
Model Output ◽  
Global Sensitivity ◽  
Sparse Polynomial ◽  
Sensitivity Indices ◽  
Highly Nonlinear

<p>Due to their highly nonlinear, non-monotonic or even discontinuous behavior, sensitivity analysis of final repository models can be a demanding task. Most of the output of repository models is typically distributed over several orders of magnitude and highly skewed. Many values of a probabilistic investigation are very low or even zero. Although this is desirable in view of repository safety it can distort the evidence of sensitivity analysis. For the safety assessment of the system, the highest values of outputs are mainly essential and if those are only a few, their dependence on specific parameters may appear insignificant. By applying a transformation, different model output values are differently weighed, according to their magnitude, in sensitivity analysis. Probabilistic methods of higher-order sensitivity analysis, applied on appropriately transformed model output values, provide a possibility for more robust identification of relevant parameters and their interactions. This type of sensitivity analysis is typically done by decomposing the total unconditional variance of the model output into partial variances corresponding to different terms in the ANOVA decomposition. From this, sensitivity indices of increasing order can be computed. The key indices used most often are the first-order index (SI1) and the total-order index (SIT). SI1 refers to the individual impact of one parameter on the model and SIT represents the total effect of one parameter on the output in interactions with all other parameters. The second-order sensitivity indices (SI2) describe the interactions between two model parameters.</p><p>In this work global sensitivity analysis has been performed with three different kinds of output transformations (log, shifted and Box-Cox transformation) and two metamodeling approaches, namely the Random-Sampling High Dimensional Model Representation (RS-HDMR) [1] and the Bayesian Sparse PCE (BSPCE) [2] approaches. Both approaches are implemented in the SobolGSA software [3, 4] which was used in this work. We analyzed the time-dependent output with two approaches for sensitivity analysis, i.e., the pointwise and generalized approaches. With the pointwise approach, the output at each time step is analyzed independently. The generalized approach considers averaged output contributions at all previous time steps in the analysis of the current step. Obtained results indicate that robustness can be improved by using appropriate transformations and choice of coefficients for the transformation and the metamodel.</p><p>[1] M. Zuniga, S. Kucherenko, N. Shah (2013). Metamodelling with independent and dependent inputs. Computer Physics Communications, 184 (6): 1570-1580.</p><p>[2] Q. Shao, A. Younes, M. Fahs, T.A. Mara (2017). Bayesian sparse polynomial chaos expansion for global sensitivity analysis. Computer Methods in Applied Mechanics and Engineering, 318: 474-496.</p><p>[3] S. M. Spiessl, S. Kucherenko, D.-A. Becker, O. Zaccheus (2018). Higher-order sensitivity analysis of a final repository model with discontinuous behaviour. Reliability Engineering and System Safety, doi: https://doi.org/10.1016/j.ress.2018.12.004.</p><p>[4] SobolGSA software (2021). User manual https://www.imperial.ac.uk/process-systems-engineering/research/free-software/sobolgsa-software/.</p>

scholarly journals Exploring the Environmental Exposure to Methoxychlor, α-HCH and Endosulfan–sulfate Residues in Lake Naivasha (Kenya) Using a Multimedia Fate Modeling Approach

2020 ◽  
Vol 17 (8) ◽  
pp. 2727
Author(s):  
Yasser Abbasi ◽  
Chris M. Mannaerts
Keyword(s):  
Sensitivity Analysis ◽  
Water Column ◽  
Environmental Exposure ◽  
Pesticide Residues ◽  
Half Life ◽  
Mass Fraction ◽  
Passive Sampling ◽  
Lake Naivasha ◽  
Endosulfan Sulfate ◽  
Modeling Approach

Distribution of pesticide residues in the environment and their transport to surface water bodies is one of the most important environmental challenges. Fate of pesticides in the complex environments, especially in aquatic phases such as lakes and rivers, is governed by the main properties of the contaminants and the environmental properties. In this study, a multimedia mass modeling approach using the Quantitative Water Air Sediment Interaction (QWASI) model was applied to explore the fate of organochlorine pesticide residues of methoxychlor, α-HCH and endosulfan–sulfate in the lake Naivasha (Kenya). The required physicochemical data of the pesticides such as molar mass, vapor pressure, air–water partitioning coefficient (KAW), solubility, and the Henry’s law constant were provided as the inputs of the model. The environment data also were collected using field measurements and taken from the literature. The sensitivity analysis of the model was applied using One At a Time (OAT) approach and calibrated using measured pesticide residues by passive sampling method. Finally, the calibrated model was used to estimate the fate and distribution of the pesticide residues in different media of the lake. The result of sensitivity analysis showed that the five most sensitive parameters were KOC, logKow, half-life of the pollutants in water, half-life of the pollutants in sediment, and KAW. The variations of outputs for the three studied pesticide residues against inputs were noticeably different. For example, the range of changes in the concentration of α-HCH residue was between 96% to 102%, while for methoxychlor and endosulfan-sulfate it was between 65% to 125%. The results of calibration demonstrated that the model was calibrated reasonably with the R2 of 0.65 and RMSE of 16.4. It was found that methoxychlor had a mass fraction of almost 70% in water column and almost 30% of mass fraction in the sediment. In contrast, endosulfan–sulfate had highest most fraction in the water column (>99%) and just a negligible percentage in the sediment compartment. α-HCH also had the same situation like endosulfan–sulfate (e.g., 99% and 1% in water and sediment, respectively). Finally, it was concluded that the application of QWASI in combination with passive sampling technique allowed an insight to the fate process of the studied OCPs and helped actual concentration predictions. Therefore, the results of this study can also be used to perform risk assessment and investigate the environmental exposure of pesticide residues.

scholarly journals Adaptive meta-modeling-based simulation optimization in basin-scale optimum water allocation: a comparative analysis of meta-models

2015 ◽  
Vol 18 (3) ◽  
pp. 446-465 ◽  
Author(s):  
Golnazalsadat Mirfenderesgi ◽  
S. Jamshid Mousavi
Keyword(s):  
River Basin ◽  
Water Allocation ◽  
Optimization Problems ◽  
Computational Cost ◽  
Search Space ◽  
Support Vector ◽  
Meta Model ◽  
Modeling Approach ◽  
Basin Scale ◽  
Meta Modeling

Incorporating river basin simulation models in heuristic optimization algorithms can help modelers address complex, basin-scale water resource problems. We have developed a hybrid optimization-simulation model by linking a stretching particle swarm optimization (SPSO) algorithm and the MODSIM river basin decision support system (DSS), and have used the SPSO-MODSIM model to optimize water allocation at basin scale. Due to high computational cost of the SPSO-MODSIM model, we have, subsequently, used four meta-model types of artificial neural networks (ANN), support vector machines (SVM), kriging and polynomial response functions, replacing the MODSIM DSS, in an adaptively learning meta-modeling approach. The performances of the meta-models are first compared in two Ackley and Dejong benchmark functions optimization problems, and the meta-models are then evaluated by solving the Atrak river basin water allocation optimization problem in Iran. The results demonstrate that independent of the meta-model type, the sequentially space-filling meta-modeling approach can improve the performance of meta-models in the course of optimization by adaptively locating the promising regions of the search space where more samples need to be generated. However, the ANN and SVM meta-models perform better than others in saving the number of costly, original objective function evaluations.

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