Committee Machines for Hourly Water Demand Forecasting in Water Supply Systems. (8th January 2019)
- Record Type:
- Journal Article
- Title:
- Committee Machines for Hourly Water Demand Forecasting in Water Supply Systems. (8th January 2019)
- Main Title:
- Committee Machines for Hourly Water Demand Forecasting in Water Supply Systems
- Authors:
- Ambrosio, Julia K.
Brentan, Bruno M.
Herrera, Manuel
Luvizotto, Edevar
Ribeiro, Lubienska
Izquierdo, Joaquín - Other Names:
- Torres Javier Martinez Academic Editor.
- Abstract:
- Abstract : Prediction models have become essential for the improvement of decision-making processes in public management and, particularly, for water supply utilities. Accurate estimation often needs to solve multimeasurement, mixed-mode, and space-time problems, typical of many engineering applications. As a result, accurate estimation of real world variables is still one of the major problems in mathematical approximation. Several individual techniques have shown very good estimation abilities. However, none of them are free from drawbacks. This paper faces the challenge of creating accurate water demand predictive models at urban scale by using so-called committee machines, which are ensemble frameworks of single machine learning models. The proposal is able to combine models of varied nature. Specifically, this paper analyzes combinations of such techniques as multilayer perceptrons, support vector machines, extreme learning machines, random forests, adaptive neural fuzzy inference systems, and the group method for data handling. Analyses are checked on two water demand datasets from Franca (Brazil). As an ensemble tool, the combined response of a committee machine outperforms any single constituent model.
- Is Part Of:
- Mathematical problems in engineering. Volume 2019(2019)
- Journal:
- Mathematical problems in engineering
- Issue:
- Volume 2019(2019)
- Issue Display:
- Volume 2019, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 2019
- Issue:
- 2019
- Issue Sort Value:
- 2019-2019-2019-0000
- Page Start:
- Page End:
- Publication Date:
- 2019-01-08
- Subjects:
- Engineering mathematics -- Periodicals
510.2462 - Journal URLs:
- https://www.hindawi.com/journals/mpe/ ↗
http://www.gbhap-us.com/journals/238/238-top.htm ↗ - DOI:
- 10.1155/2019/9765468 ↗
- Languages:
- English
- ISSNs:
- 1024-123X
- Deposit Type:
- Legaldeposit
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 10225.xml