FORECASTING DAILY URBAN WATER DEMAND USING ARTIFICIAL NEURAL NETWORKS, A CASE STUDY OF TEHRAN URBAN WATER

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

TABESH M.; DINI M.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: WATER AND WASTEWATER Year:2010 | Volume:21 | Issue:1 (73) Page(s): 84-95

Download Full-Text

Persian Verion

View:

1,968

Download:

Cites:

Information Journal Paper

Title

FORECASTING DAILY URBAN WATER DEMAND USING ARTIFICIAL NEURAL NETWORKS, A CASE STUDY OF TEHRAN URBAN WATER

Author(s)

TABESH M. | DINI M. | Issue Writer Certificate

Keywords

URBAN WATER DEMANDQ3

FORECASTINGQ2

ARTIFICIAL NEURAL NETWORKSQ2

AVERAGE TEMPERATUREQ3

RELATIVE HUMIDITYQ3

Abstract

Water demand FORECASTING is one of the most important concerns for managers of water supply systems as the results can affect many decisions. Daily demand FORECASTING cannot be usually accomplished by mathematical functions because it is a complicated function of many variables. In this paper, neural networks are used to predict Tehran daily water demand. At first, weather data from three Tehran weather stations are weighted via the Thissen method and the effective input data parameters are selected using the regression of the weighted effective weather and consumption data. The effective parameters include daily AVERAGE TEMPERATURE, RELATIVE HUMIDITY, and last day to last week (7 days) as well as last year water consumptions. Three different ANN models are built in this stage: a three-layer model with one hidden layer including seven neurons, a four-layer model with two hidden layers including seven neurons in the first and four neurons in the second hidden layer, and a RBF three-layer model with twenty neurons in the middle layer. Comparison of the results of ANN with neuro-fuzzy and time series models shows that ANN models have a higher capability for predicting Tehran daily water consumption. Among these models, the ANN perceptron 3-layer model with a nonlinear output produced more accurate results.

Cites

A HYBRID MODEL FOR FORECASTING DAILY URBAN WATER DEMAND

References

Cite

APA: Copy

TABESH, M., & DINI, M.. (2010). FORECASTING DAILY URBAN WATER DEMAND USING ARTIFICIAL NEURAL NETWORKS, A CASE STUDY OF TEHRAN URBAN WATER. WATER AND WASTEWATER, 21(1 (73)), 84-95. SID. https://sid.ir/paper/103554/en

Vancouver: Copy

TABESH M., DINI M.. FORECASTING DAILY URBAN WATER DEMAND USING ARTIFICIAL NEURAL NETWORKS, A CASE STUDY OF TEHRAN URBAN WATER. WATER AND WASTEWATER[Internet]. 2010;21(1 (73)):84-95. Available from: https://sid.ir/paper/103554/en

IEEE: Copy

M. TABESH, and M. DINI, “FORECASTING DAILY URBAN WATER DEMAND USING ARTIFICIAL NEURAL NETWORKS, A CASE STUDY OF TEHRAN URBAN WATER,” WATER AND WASTEWATER, vol. 21, no. 1 (73), pp. 84–95, 2010, [Online]. Available: https://sid.ir/paper/103554/en

Related Journal Papers