1. On-line Learning of Predictive Kernel Models for Urban Water Demand in a Smart City.
- Author
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Herrera, M., Izquierdo, J., Pérez-Garćıa, R., and Ayala-Cabrera, D.
- Subjects
MACHINE learning ,KERNEL operating systems ,WATER demand management ,INFORMATION theory ,WATER distribution ,SUPPORT vector machines - 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]
- Published
- 2014
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