A water utility's primary purpose is to provide safe drinking
water to its customers, but it must also plan for its customers'
future water needs. A critical aspect of this planning
is predicting short-term (peak) water demands and optimizing
the water supply system to meet these demands. Jain and Ormsbee
evaluated eight models (four conventional and four artificial
intelligence [AI]) to forecast short-term water demand. The models
were developed and tested using daily water demand, daily
maximum air temperature, and daily total rainfall.
AI models outperformed the conventional models and predicted
short-term water demands during both "normal" and drought conditions
relatively accurately. These AI models can be run using commercial
software, if the system operator is experienced in using
models, or simple rule-based AI models can be easily developed
and applied using historical demand, weather data, and a standard
spreadsheet program. The authors encourage utility operators or
managers to incorporate a short-term water-demand forecasting
model into their predictions of future water needs.
Includes 25 references, tables, figures.
| Edition : | Vol. 94 - No. 7 |
| File Size : | 1
file
, 220 KB |
| Note : | This product is unavailable in Ukraine, Russia, Belarus |
| Number of Pages : | 9 |
| Published : | 07/01/2002 |
