Committee Machines for Hourly Water Demand Forecasting in Water Supply Systems
Ambrosio, Julia K.
Brentan, Bruno M.
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Ambrosio, J. K., Brentan, B. M., Herrera, M., Luvizotto, E., Ribeiro, L., & Izquierdo, J. (2019). Committee Machines for Hourly Water Demand Forecasting in Water Supply Systems. [Journal Article]. https://doi.org/10.1155/2019/9765468
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.
External DOI: https://doi.org/10.1155/2019/9765468
This record's DOI: https://doi.org/10.17863/CAM.35239
Rights Holder: Copyright © 2019 Julia K. Ambrosio et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.