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Smart city article details

Title	On-Line Learning Of Predictive Kernel Models For Urban Water Demand In A Smart City
ID_Doc	39987
Authors	Herrera, M; Izquierdo, J; Pérez-García, R; Ayala-Cabrera, D
Year	2014
Published	12TH INTERNATIONAL CONFERENCE ON COMPUTING AND CONTROL FOR THE WATER INDUSTRY, CCWI2013, 70
DOI	http://dx.doi.org/10.1016/j.proeng.2014.02.086
Abstract	This paper proposes a multiple kernel regression (MKr) to predict water demand in the presence of a continuous source of information. 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.
Author Keywords	Smart cities; urban water demand; kernel regression; on-line learning

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