Machine learning models for forecasting water demand for the Metropolitan Region of Salvador, Bahia
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-08842-0.pdf
Reference38 articles.
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2. Alvisi S, Franchini M, Marinelli A (2007) A short-term, pattern-based model for water-demand forecasting. J Hydroinformat 9(1):39–50. https://doi.org/10.2166/hydro.2006.016
3. Babu CN, Reddy BE (2014) A moving-average filter based hybrid Arima-ANN model for forecasting time series data. Appl Soft Comput 23:27–38. https://doi.org/10.1016/j.asoc.2014.05.028
4. Bakker M, van Duist H, van Schagen K et al (2014) Improving the performance of water demand forecasting models by using weather input. Proced Eng 70:93–102. https://doi.org/10.1016/j.proeng.2014.02.012
5. Bougadis J, Adamowski K, Diduch R (2005) Short-term municipal water demand forecasting. Hydrol Process 19(1):137–148. https://doi.org/10.1002/hyp.5763
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