Application of parallel computing to stochastic parameter estimation in environmental models
Parameter estimation or model calibration is a common problem in many areas of process modeling, both in on-line applications such as real-time flood forecasting, and in off-line applications such as the modeling of reaction kinetics and phase equilibrium. The goal is to determine values of model pa...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/96064 http://hdl.handle.net/10220/10141 |
| _version_ | 1811687520411320320 |
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| author | Vrugt, Jasper A. Ó Nualláin, Breanndán Robinson, Bruce A. Bouten, Willem Dekker, Stefan C. Sloot, Peter M. A. |
| author2 | School of Computer Engineering |
| author_facet | School of Computer Engineering Vrugt, Jasper A. Ó Nualláin, Breanndán Robinson, Bruce A. Bouten, Willem Dekker, Stefan C. Sloot, Peter M. A. |
| author_sort | Vrugt, Jasper A. |
| collection | NTU |
| description | Parameter estimation or model calibration is a common problem in many areas of process modeling, both in on-line applications such as real-time flood forecasting, and in off-line applications such as the modeling of reaction kinetics and phase equilibrium. The goal is to determine values of model parameters that provide the best fit to measured data, generally based on some type of least-squares or maximum likelihood criterion. Usually, this requires the solution of a non-linear and frequently non-convex optimization problem. In this paper we describe a user-friendly, computationally efficient parallel implementation of the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm for stochastic estimation of parameters in environmental models. Our parallel implementation takes better advantage of the computational power of a distributed computer system. Three case studies of increasing complexity demonstrate that parallel parameter estimation results in a considerable time savings when compared with traditional sequential optimization runs. The proposed method therefore provides an ideal means to solve complex optimization problems. |
| first_indexed | 2024-10-01T05:17:37Z |
| format | Journal Article |
| id | ntu-10356/96064 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:17:37Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/960642020-05-28T07:17:17Z Application of parallel computing to stochastic parameter estimation in environmental models Vrugt, Jasper A. Ó Nualláin, Breanndán Robinson, Bruce A. Bouten, Willem Dekker, Stefan C. Sloot, Peter M. A. School of Computer Engineering Parameter estimation or model calibration is a common problem in many areas of process modeling, both in on-line applications such as real-time flood forecasting, and in off-line applications such as the modeling of reaction kinetics and phase equilibrium. The goal is to determine values of model parameters that provide the best fit to measured data, generally based on some type of least-squares or maximum likelihood criterion. Usually, this requires the solution of a non-linear and frequently non-convex optimization problem. In this paper we describe a user-friendly, computationally efficient parallel implementation of the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm for stochastic estimation of parameters in environmental models. Our parallel implementation takes better advantage of the computational power of a distributed computer system. Three case studies of increasing complexity demonstrate that parallel parameter estimation results in a considerable time savings when compared with traditional sequential optimization runs. The proposed method therefore provides an ideal means to solve complex optimization problems. 2013-06-11T01:43:50Z 2019-12-06T19:25:04Z 2013-06-11T01:43:50Z 2019-12-06T19:25:04Z 2005 2005 Journal Article Vrugt, J. A., Ó Nualláin, B., Robinson, B. A., Bouten, W., Dekker, S. C., & Sloot, P. M. A. (2005). Application of parallel computing to stochastic parameter estimation in environmental models. Computers & Geosciences, 32(8), 1139-1155. https://hdl.handle.net/10356/96064 http://hdl.handle.net/10220/10141 10.1016/j.cageo.2005.10.015 en Computers & geosciences © 2005 Elsevier Ltd. |
| spellingShingle | Vrugt, Jasper A. Ó Nualláin, Breanndán Robinson, Bruce A. Bouten, Willem Dekker, Stefan C. Sloot, Peter M. A. Application of parallel computing to stochastic parameter estimation in environmental models |
| title | Application of parallel computing to stochastic parameter estimation in environmental models |
| title_full | Application of parallel computing to stochastic parameter estimation in environmental models |
| title_fullStr | Application of parallel computing to stochastic parameter estimation in environmental models |
| title_full_unstemmed | Application of parallel computing to stochastic parameter estimation in environmental models |
| title_short | Application of parallel computing to stochastic parameter estimation in environmental models |
| title_sort | application of parallel computing to stochastic parameter estimation in environmental models |
| url | https://hdl.handle.net/10356/96064 http://hdl.handle.net/10220/10141 |
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