Automatic Mapping of Monitoring Data

Lophaven, Søren Nymand; Nielsen, Hans Bruun; Sondergaard, Jacob

doi:10.2104/ag050013

Cited by 95 publications

(137 citation statements)

References 0 publications

Supporting

Mentioning

106

Contrasting

Unclassified

Order By: Relevance

“…[16] The regression-Gaussian model in equation (1) was adopted from Sacks et al [1989] and implemented with the software package developed by Lophaven et al [2002]. Best linear unbiased predictor (BLUP) methodology was used to determine the coefficients of equation (1).…”

Section: Performance Mappingmentioning

confidence: 99%

“…The minimum number of input-output data sets that is mathematically required to develop the mapping function and provide confidence intervals is n 1 2 (n is the dimension of the function input space). Interested readers are referred to Sacks et al [1989] and Lophaven et al [2002] for details on how to develop and fit the model. Notably, unlike regression models, the mapping system developed is an exact interpolator for the input-output sets used for developing/updating the system-the system would generate the exact model performance (i.e., full-period MSE values) for the parameter sets that have been already evaluated over the full data period and included in the input-output sets for the fitting of the mapping system.…”

Section: Performance Mappingmentioning

confidence: 99%

See 1 more Smart Citation

An efficient framework for hydrologic model calibration on long data periods

Razavi

Tolson

2013

Water Resour. Res.

View full text Add to dashboard Cite

.[1] Long periods of hydrologic data records have become available in many watersheds around the globe. Hydrologic model calibration on such long, full-length data periods is typically deemed the most robust approach for calibration but at larger computational costs. Determination of a representative short period as a ''surrogate'' of a long data period that sufficiently embeds its information content is not trivial and is a challenging research question. The representativeness of such a short period is not only a function of data characteristics but also model and calibration error function dependent. Unlike previous studies, this study goes beyond identifying the best surrogate data period to be used in model calibration and proposes an efficient framework that calibrates the hydrologic model to full-length data while running the model only on a short period for the majority of the candidate parameter sets. To this end, a mapping system is developed to approximate the model performance on the full-length data period based on the model performance for the short data period. The basic concepts and the promise of the framework are demonstrated through a computationally expensive hydrologic model case study. Three calibration approaches, namely calibration solely to a surrogate period, calibration to the full period, and calibration through the proposed framework, are evaluated and compared. Results show that within the same computational budget, the proposed framework leads to improved or equal calibration performance compared to the two conventional approaches. Results also indicate that model calibration solely to a short data period may lead to a range of performances from poor to very well depending on the representativeness of the short data period which is typically not known a priori.

show abstract

Section: Performance Mappingmentioning

confidence: 99%

Section: Performance Mappingmentioning

confidence: 99%

An efficient framework for hydrologic model calibration on long data periods

Razavi

Tolson

2013

Water Resour. Res.

View full text Add to dashboard Cite

show abstract

“…The used metamodels are listed in Table 4. The used kriging toolbox has been developed by Lophaven et al (2002) and corresponds with the description given in Section 2.1.1. The RBF has been implemented following the proposal given in Section 3.…”

Section: Optimization Algorithmmentioning

confidence: 99%

Sequential improvement for robust optimization using an uncertainty measure for radial basis functions

Havinga

Boogaard

Klaseboer

2016

Struct Multidisc Optim

View full text Add to dashboard Cite

The performance of the sequential metamodel based optimization procedure depends strongly on the chosen building blocks for the algorithm, such as the used metamodeling method and sequential improvement criterion. In this study, the effect of these choices on the efficiency of the robust optimization procedure is investigated. A novel sequential improvement criterion for robust optimization is proposed, as well as an improved implementation of radial basis function interpolation suitable for sequential optimization. The leave-one-out cross-validation measure is used to estimate the uncertainty of the radial basis function metamodel. The metamodeling methods and sequential improvement criteria are compared, based on a test with Gaussian random fields as well as on the optimization of a strip bending process with five design variables and two noise variables. For this process, better results are obtained in the runs with the novel sequential improvement criterion as well as with the novel radial basis function implementation, compared to the runs with conventional sequential improvement criteria and kriging interpolation.

show abstract

“…(6). This could be done by maximum likelihood estimation [26] or schema search method [27]. However, both the methods suffer from specific problems.…”

Section: Construction Of Kriging Model and Accuracy Analysismentioning

confidence: 99%

Multi-objective optimization of the streamlined head of high-speed trains based on the Kriging model

Yao

Guo

Sun

et al. 2012

Sci. China Technol. Sci.

View full text Add to dashboard Cite

As the running speed of high-speed trains increases, aerodynamic drag becomes the key factor which limits the further increase of the running speed and energy consumption. Aerodynamic lift of the trailing car also becomes the key force which affects the amenity and safety of the train. In the present paper, a simplified CRH380A high-speed train with three carriages is chosen as the model in order to optimize aerodynamic drag of the total train and aerodynamic lift of the trailing car. A constrained multi-objective optimization design of the aerodynamic head shape of high-speed trains based on adaptive non-dominated sorting genetic algorithm is also developed combining local function three-dimensional parametric approach and central Latin hypercube sampling method with maximin criteria based on the iterative local search algorithm. The results show that local function parametric approach can be well applied to optimal design of complex three-dimensional aerodynamic shape, and the adaptive non-dominated sorting genetic algorithm can be more accurate and efficient to find the Pareto front. After optimization the aerodynamic drag of the simplified train with three carriages is reduced by 3.2%, and the lift coefficient of the trailing car by 8.24%, the volume of the streamlined head by 2.16%; the aerodynamic drag of the real prototype CRH380A is reduced by 2.26%, lift coefficient of the trailing car by 19.67%. The variation of aerodynamic performance between the simplified train and the true train is mainly concentrated in the deformation region of the nose cone and tail cone. The optimization approach proposed in the present paper is simple yet efficient, and sheds lights on the constrained multi-objective engineering optimization design of aerodynamic shape of high-speed trains.

show abstract

Automatic Mapping of Monitoring Data

Cited by 95 publications

References 0 publications

An efficient framework for hydrologic model calibration on long data periods

An efficient framework for hydrologic model calibration on long data periods

Sequential improvement for robust optimization using an uncertainty measure for radial basis functions

Multi-objective optimization of the streamlined head of high-speed trains based on the Kriging model

Contact Info

Product

Resources

About