Optimization of concentration control by evolution strategies: Formulation, application, and assessment of remedial solutions

Bayer, Peter; Finkel, Michael

doi:10.1029/2005wr004753

Cited by 42 publications

(20 citation statements)

References 53 publications

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“…The applied calibration scheme is based on the parameter estimation tool PEST (Doherty, ). The current calibration utilizes the global optimization scheme within PEST based on covariance matrix adaptation estimation strategies (CMA‐ES) as introduced by Hansen and Ostermeier () and later applied by Bayer and Finkel () to calibrate a groundwater model against concentration. The CMA‐ES utilizes a stochastic sampling approach based on updated probability fields and has proven very efficient in comparison with other stochastic optimization algorithms in hydrological model calibrations both regarding convergence speed and finding the lowest minimum (Arsenault, Poulin, Cote, & Brissette, ).…”

Section: Optimization Schemementioning

confidence: 99%

Moving beyond run‐off calibration—Multivariable optimization of a surface–subsurface–atmosphere model

Stisen

Koch

Sonnenborg

et al. 2018

Hydrological Processes

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Spatially distributed hydrological models are traditionally calibrated and evaluated against few spatially aggregated observations such as river discharge. This model evaluation approach does not enable an assessment of the model predictive capabilities of other hydrological states and fluxes nor does it give any insight into the model ability to mimic the spatial patterns within a catchment. The current study explores a multivariable optimization of a complex coupled surface–subsurface–atmosphere model at the catchment scale in an attempt to move beyond simple run‐off calibration. The model is evaluated against five independent observational data sets of discharge (Q), hydraulic head (h), actual evapotranspiration (ET), soil moisture (SM), and remotely sensed land surface temperature (LST). It is shown that a balanced optimization can be achieved where errors on objective functions for all five observation data sets can be reduced simultaneously. Additionally, the multivariable calibration proved more robust, compared with calibration against Q and h only, during the validation period, even for Q and h. The current parameterization and calibration framework was mainly suitable for reducing model biases and allowed only limited improvements in the spatio‐temporal patterns of the model simulations. This points towards development of better parametrization schemes that will allow simulated spatial patterns to adjust during calibration. Additionally, analysis showed that systematic spatial patterns in the errors of the LST maps could be a very valuable diagnostic tool for assessing deficiencies in the model structure, spatial parameterization, or process description.

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Section: Optimization Schemementioning

confidence: 99%

Moving beyond run‐off calibration—Multivariable optimization of a surface–subsurface–atmosphere model

Stisen

Koch

Sonnenborg

et al. 2018

Hydrological Processes

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“…Additionally, the CMAES guided search in the parameter space makes the algorithm less computationally demanding than other global optimization approaches, which enumerate a large number of possible solutions (e.g. Monte CarloMarkov chain methods) (Bayer and Finkel, 2007). In order to keep computational demands low and to avoid overfitting by a very small sample size, we perform calibration for a subset of 1000 randomly chosen grid cells.…”

Section: Multi-criteria Calibrationmentioning

confidence: 99%

Understanding terrestrial water storage variations in northern latitudes across scales

Trautmann

Koirala

Eicker

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. The GRACE satellites provide signals of total terrestrial water storage (TWS) variations over large spatial domains at seasonal to inter-annual timescales. While the GRACE data have been extensively and successfully used to assess spatio-temporal changes in TWS, little effort has been made to quantify the relative contributions of snowpacks, soil moisture, and other components to the integrated TWS signal across northern latitudes, which is essential to gain a better insight into the underlying hydrological processes. Therefore, this study aims to assess which storage component dominates the spatio-temporal patterns of TWS variations in the humid regions of northern mid-to high latitudes.To do so, we constrained a rather parsimonious hydrological model with multiple state-of-the-art Earth observation products including GRACE TWS anomalies, estimates of snow water equivalent, evapotranspiration fluxes, and gridded runoff estimates. The optimized model demonstrates good agreement with observed hydrological spatio-temporal patterns and was used to assess the relative contributions of solid (snowpack) versus liquid (soil moisture, retained water) storage components to total TWS variations. In particular, we analysed whether the same storage component dominates TWS variations at seasonal and inter-annual temporal scales, and whether the dominating component is consistent across small to large spatial scales.Consistent with previous studies, we show that snow dynamics control seasonal TWS variations across all spatial scales in the northern mid-to high latitudes. In contrast, we find that inter-annual variations of TWS are dominated by liquid water storages at all spatial scales. The relative contribution of snow to inter-annual TWS variations, though, increases when the spatial domain over which the storages are averaged becomes larger. This is due to a stronger spatial coherence of snow dynamics that are mainly driven by temperature, as opposed to spatially more heterogeneous liquid water anomalies, that cancel out when averaged over a larger spatial domain. The findings first highlight the effectiveness of our model-data fusion approach that jointly interprets multiple Earth observation data streams with a simple model. Secondly, they reveal that the determinants of TWS variations in snow-affected northern latitudes are scale-dependent. In particular, they seem to be not merely driven by snow variability, but rather are determined by liquid water storages on interannual timescales. We conclude that inferred driving mechanisms of TWS cannot simply be transferred from one scale to another, which is of particular relevance for understanding the short-and long-term variability of water resources.

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“…Solving the MINLP problem by gradient-based optimization algorithms would be very complicated because the solution needs combinatorial optimization algorithms. Genetic algorithms or evolutionary algorithms are derivative-free algorithms and have been proven to be efficient optimization approaches for groundwater remediation problems involving integer variables (McKinney and Lin 1994; Guan and Aral 1999;Park and Aral 2004;Bayer and Finkel 2004;Bayer and Finkel 2007;Singh and Minsker 2008). This study employs a GA with binary chromosomes to search for the pump rates and the binary values of the scheduling variables.…”

Section: ……… ………mentioning

confidence: 99%

Bayesian model averaging assessment on groundwater management under model structure uncertainty

Tsai

2010

Stoch Environ Res Risk Assess

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This study introduces Bayesian model averaging (BMA) to deal with model structure uncertainty in groundwater management decisions. A robust optimized policy should take into account model parameter uncertainty as well as uncertainty in imprecise model structure. Due to a limited amount of groundwater head data and hydraulic conductivity data, multiple simulation models are developed based on different head boundary condition values and semivariogram models of hydraulic conductivity. Instead of selecting the best simulation model, a variance-window-based BMA method is introduced to the management model to utilize all simulation models to predict chloride concentration. Given different semivariogram models, the spatially correlated hydraulic conductivity distributions are estimated by the generalized parameterization (GP) method that combines the Voronoi zones and the ordinary kriging (OK) estimates. The model weights of BMA are estimated by the Bayesian information criterion (BIC) and the variance window in the maximum likelihood estimation. The simulation models are then weighted to predict chloride concentrations within the constraints of the management model. The methodology is implemented to manage saltwater intrusion in the ''1,500-foot'' sand aquifer in the Baton Rouge area, Louisiana. The management model aims to obtain optimal joint operations of the hydraulic barrier system and the saltwater extraction system to mitigate saltwater intrusion. A genetic algorithm (GA) is used to obtain the optimal injection and extraction policies. Using the BMA predictions, higher injection rates and pumping rates are needed to cover more constraint violations, which do not occur if a single best model is used.

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Optimization of concentration control by evolution strategies: Formulation, application, and assessment of remedial solutions

Cited by 42 publications

References 53 publications

Moving beyond run‐off calibration—Multivariable optimization of a surface–subsurface–atmosphere model

Moving beyond run‐off calibration—Multivariable optimization of a surface–subsurface–atmosphere model

Understanding terrestrial water storage variations in northern latitudes across scales

Bayesian model averaging assessment on groundwater management under model structure uncertainty

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