Forecasting urban household water demand with statistical and machine learning methods using large space-time data: A Comparative study
| Autor: | Isaac Duerr, Mackenzie J. Boyer, Ray Bai, Michael D. Dukes, Nikolay Bliznyuk, Hunter R. Merrill, Chuan Wang |
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| Rok vydání: | 2018 |
| Předmět: |
Environmental Engineering
Computer science business.industry Ecological Modeling Space time 0208 environmental biotechnology Water supply Statistical model 02 engineering and technology Machine learning computer.software_genre 01 natural sciences 020801 environmental engineering Water demand 010104 statistics & probability Order (exchange) Feature (machine learning) Artificial intelligence 0101 mathematics Uncertainty quantification business computer Software Water use |
| Zdroj: | Environmental Modelling & Software. 102:29-38 |
| ISSN: | 1364-8152 |
| Popis: | Forecasts of water use are crucial to efficiently manage water utilities to meet growing demand in urban areas. Improved household-level forecasts may be useful to water managers in order to accurately identify, and potentially target for management and conservation, low-efficiency homes and relative high-demand customers. Advanced machine learning (ML) techniques are available for feature-based predictions, but many of these methods ignore multiscale spatiotemporal associations that may improve prediction accuracy. We use a large dataset collected by Tampa Bay Water, a regional water wholesaler in southwest Florida, to evaluate an array of spatiotemporal statistical models and ML algorithms using out-of-sample prediction accuracy and uncertainty quantification to find the best tools for forecasting household-level monthly water demand. Time series models appear to provide the best short-term forecasts, indicating that the temporal dynamics of water use are more important for prediction than any exogenous features. |
| Databáze: | OpenAIRE |
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