Article

Service-oriented grid computation for large-scale parameter estimation in complex environmental modeling

Authors:

Kejing He,

Shoubin Dong,

Li ZhengAuthors Info & Claims

SAC '06: Proceedings of the 2006 ACM symposium on Applied computing

Pages 741 - 745

https://doi.org/10.1145/1141277.1141449

Published: 23 April 2006 Publication History

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Abstract

Complex environmental modeling often involves a large number of unknown physical and ecological parameters. Parameter estimation is one of the most difficult steps in many modeling activities. In this paper we present a service-oriented framework, named GGPE-G (Grid-enabled Global optimization for General Parameter Estimation), for efficient parameter estimation in heterogeneous, distributed systems. Being presented as services, the optimization algorithms, the physical and ecological process models and clients can interact with each other by XML message interactions. The proposed approach supports a generic parameter estimation procedure and can be easily applied to different modeling environment. In this paper, we explain the design, architecture, and implementation of GGPE-G in details. We also apply GGPE-G to a complex soil-water-atmosphere-plant modeling system to demonstrate its utility and efficiency.

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Cited By

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Wendt KDenham MCortés AMargalef T(2011)Evolutionary Optimisation Techniques to Estimate Input Parameters in Environmental Emergency ModellingComputational Optimization and Applications in Engineering and Industry10.1007/978-3-642-20986-4_5(125-143)Online publication date: 2011
https://doi.org/10.1007/978-3-642-20986-4_5
Kejing He Xiancheng Xu Qiang Yue (2008)A secure, lossless, and compressed Base62 encoding2008 11th IEEE Singapore International Conference on Communication Systems10.1109/ICCS.2008.4737287(761-765)Online publication date: Nov-2008
https://doi.org/10.1109/ICCS.2008.4737287
He KDong SHe JTang L(2006)GSGCP-FEMProceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing10.1109/APSCC.2006.64(458-465)Online publication date: 12-Dec-2006
https://dl.acm.org/doi/10.1109/APSCC.2006.64

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Published In

SAC '06: Proceedings of the 2006 ACM symposium on Applied computing

April 2006

1967 pages

ISBN:1595931082

DOI:10.1145/1141277

Conference Chair:
Hisham M. Haddad
Kennesaw State University, Kennesaw, Georgia

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 23 April 2006

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SAC06: The 2006 ACM Symposium on Applied Computing

April 23 - 27, 2006

Dijon, France

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

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Wendt KDenham MCortés AMargalef T(2011)Evolutionary Optimisation Techniques to Estimate Input Parameters in Environmental Emergency ModellingComputational Optimization and Applications in Engineering and Industry10.1007/978-3-642-20986-4_5(125-143)Online publication date: 2011
https://doi.org/10.1007/978-3-642-20986-4_5
Kejing He Xiancheng Xu Qiang Yue (2008)A secure, lossless, and compressed Base62 encoding2008 11th IEEE Singapore International Conference on Communication Systems10.1109/ICCS.2008.4737287(761-765)Online publication date: Nov-2008
https://doi.org/10.1109/ICCS.2008.4737287
He KDong SHe JTang L(2006)GSGCP-FEMProceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing10.1109/APSCC.2006.64(458-465)Online publication date: 12-Dec-2006
https://dl.acm.org/doi/10.1109/APSCC.2006.64

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Grid Analysis Environment Service Architecture for Structured Modeling

Complex-valued Bayesian parameter estimation via Markov chain Monte Carlo

Maximum-Likelihood Estimation, the CramÉr–Rao Bound, and the Method of Scoring With Parameter Constraints