Predicting time-series for water demand in the big data environment using statistical methods, machine learning and the novel analog methodology dynamic time scan forecasting
The specialized literature on water demand forecasting indicates that successful predicting models are based on soft computing approaches such as neural networks, fuzzy systems, evolutionary computing, support vector machines and hybrid models. However, soft computing models are extremely sensitive...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IWA Publishing
2023-02-01
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| Series: | Water Supply |
| Subjects: | |
| Online Access: | http://ws.iwaponline.com/content/23/2/624 |