Please use this identifier to cite or link to this item:
https://scholarbank.nus.edu.sg/handle/10635/128684
DC Field | Value | |
---|---|---|
dc.title | Improved dynamic emulation modeling by time series clustering: The case study of Marina Reservoir, Singapore | |
dc.contributor.author | Galelli, S. | |
dc.contributor.author | Caietti Marin, S. | |
dc.contributor.author | Castelletti, A. | |
dc.contributor.author | Eikaas, H. | |
dc.date.accessioned | 2016-10-19T08:44:01Z | |
dc.date.available | 2016-10-19T08:44:01Z | |
dc.date.issued | 2012 | |
dc.identifier.citation | Galelli, S., Caietti Marin, S., Castelletti, A., Eikaas, H. (2012). Improved dynamic emulation modeling by time series clustering: The case study of Marina Reservoir, Singapore. iEMSs 2012 - Managing Resources of a Limited Planet: Proceedings of the 6th Biennial Meeting of the International Environmental Modelling and Software Society : 317-324. ScholarBank@NUS Repository. | |
dc.identifier.isbn | 9788890357428 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/128684 | |
dc.description.abstract | Dynamic Emulation Modelling (DEMo) is emerging as a viable solution to combine computationally intensive simulation models and dynamic optimization algorithms. A dynamic emulator is a low order surrogate of the simulation model identified over a sample data set generated by the original simulation model itself. When applied to large 3D models, any DEMo exercise does require a pre-processing of the exogenous drivers and state variables in order to reduce, by spatial aggregation, the high number of candidate variables to appear in the final emulator. This work describes a hybrid clustering-variable selection approach to automatically discover compact and relevant representations of high-dimensional data sets. Time series clustering is adopted to identify spatial structures by objectively organizing data into homogenous groups, where the within-group-object similarity is minimized. In particular, the proposed approach relies on a hierarchical agglomerative clustering method, which starts by placing each time-series in its own cluster, and then merges clusters into larger clusters, until a compact, yet informative, representation of the original variables can be processed with the Recursive Variable Selection - Iterative Input Selection algorithm, in order to single out the most relevant clusters. The approach is demonstrated on a real-world case study concerning the reduction of Delft3D, a spatially distributed hydrodynamic model used to simulate salt intrusion dynamics in the tropical lake of Marina Reservoir, Singapore. Results show that the proposed approach permits a parsimonious, though accurate, characterization of salinity concentration. | |
dc.source | Scopus | |
dc.subject | Data-driven models | |
dc.subject | Dynamic emulation modelling | |
dc.subject | Physically-based models | |
dc.subject | Time series clustering | |
dc.subject | Variable selection | |
dc.type | Conference Paper | |
dc.contributor.department | SINGAPORE-DELFT WATER ALLIANCE | |
dc.description.sourcetitle | iEMSs 2012 - Managing Resources of a Limited Planet: Proceedings of the 6th Biennial Meeting of the International Environmental Modelling and Software Society | |
dc.description.page | 317-324 | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Staff Publications |
Show simple item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.