Abstract
This paper proposes a new hybrid SVR-ANN model for water demand forecasting. Where an adaptation of the methodology proposed by Zhang (Neurocomputing 50:159–175, 2003) is used to decompose the time series of 10 reservoirs that supply the Metropolitan Region of Salvador (RMS). The data used are from the historical consumption from January 2017 to February 2022, obtained from the local supply company, Empresa Baiana de Águas e Saneamento, and meteorological data obtained from the National Institute of Meteorology of Brazil. The results demonstrated the feasibility of using the proposed model, compared to other traditional models such as the multilayer perceptron (MLP), support vector regression (SVR), short long-term memory (LSTM) and autoregressive integrated moving average (ARIMA).
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The methods employed in this paper are available on the GitHub repository (https://github.com/edmilsondejesus/waterdemand/) to allow verification and replicability of the research.
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Material preparation, data collection, methodology, visualization, investigation and original draft of the paper were written by Edmilson dos Santos de Jesus. Conceptualization, supervision, reviewing and editing were written by Gecynalda Soares Gomes. All authors commented on the previous versions of the paper. All authors read and approved the final paper.
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Appendix A: Methods and models used to forecasting water demand
Appendix A: Methods and models used to forecasting water demand
See Appendix Table 8.
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Santos de Jesus, E.d., Silva Gomes, G.S.d. Machine learning models for forecasting water demand for the Metropolitan Region of Salvador, Bahia. Neural Comput & Applic 35, 19669–19683 (2023). https://doi.org/10.1007/s00521-023-08842-0
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DOI: https://doi.org/10.1007/s00521-023-08842-0