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On-line Learning of Predictive Kernel Models for Urban Water Demand in a Smart City.
- Source :
- Procedia Engineering; Apr2014, Vol. 70, p791-799, 9p
- Publication Year :
- 2014
-
Abstract
- Abstract: This paper proposes a multiple kernel regression (MKr) to predict water demand in the presence of a continuous source of infor- mation. MKr extends the simple support vector regression (SVR) to a combination of kernels from as many distinct types as kinds of input data are available. In addition, two on-line learning methods to obtain real time predictions as new data arrives to the system are tested by a real-world case study. The accuracy and computational efficiency of the results indicate that our proposal is a suitable tool for making adequate management decisions in the smart cities environment. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 18777058
- Volume :
- 70
- Database :
- Supplemental Index
- Journal :
- Procedia Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 95724574
- Full Text :
- https://doi.org/10.1016/j.proeng.2014.02.086