PREDICTING THE LONGITUDINAL DISPERSION COEFFICIENT IN NATURAL STREAMS USING DEVELOPED ARTIFICIAL NEURAL NETWORK MODEL

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.

NOURI ROUH ELAH; KARBASI A.R.; MAHDIZADEH H.

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

Journal Paper

Paper Information

Journal: WATER AND WASTEWATER Year:2011 | Volume:21 | Issue:4 (76) Page(s): 104-109

Download Full-Text

Persian Verion

View:

953

Download:

Cites:

Information Journal Paper

Title

PREDICTING THE LONGITUDINAL DISPERSION COEFFICIENT IN NATURAL STREAMS USING DEVELOPED ARTIFICIAL NEURAL NETWORK MODEL

Author(s)

NOURI ROUH ELAH | KARBASI A.R. | MAHDIZADEH H. | Issue Writer Certificate

Keywords

LONGITUDINAL DISPERSION COEFFICIENTQ3

ARTIFICIAL NEURAL NETWORKQ2

QUASI-NEWTON TRAINING FUNCTIONSQ3

PREDICTIONQ2

Abstract

The main objective of the present work is to predict the LONGITUDINAL DISPERSION COEFFICIENT in natural streams using a neural network (NN) model which was developed based on QUASI-NEWTON TRAINING FUNCTIONS. For this reason, we used the hydraulic and geometric data easily obtained in natural streams. A total number of 100 data sets was used which were split into three subsets: training, validation, and testing sets. The most cited literature in the field was first reviewed in an attempt to identify possible deficiencies and inadequacies in previous studies. In a second stage, a new approach less commonly used by researchers, i.e. the NN model based on Quasi- Newton training functions was employed for predicting the LONGITUDINAL DISPERSION COEFFICIENT in natural streams. Finally, the effect of Quasi-Newton training function on the performance of the NN model was investigated and the best architecture was selected for the model developed. The results obtained in this study showed that the proposed model enjoys a satisfactory level of accuracy. The two statistics of the model, i.e. determination coefficient and mean absolute error in testing step, were found to be equal to 0.85 and 53, respectively.

Cites

No record.

References

No record.

Cite

APA: Copy

NOURI, ROUH ELAH, KARBASI, A.R., & MAHDIZADEH, H.. (2011). PREDICTING THE LONGITUDINAL DISPERSION COEFFICIENT IN NATURAL STREAMS USING DEVELOPED ARTIFICIAL NEURAL NETWORK MODEL. WATER AND WASTEWATER, 21(4 (76)), 104-109. SID. https://sid.ir/paper/104062/en

Vancouver: Copy

NOURI ROUH ELAH, KARBASI A.R., MAHDIZADEH H.. PREDICTING THE LONGITUDINAL DISPERSION COEFFICIENT IN NATURAL STREAMS USING DEVELOPED ARTIFICIAL NEURAL NETWORK MODEL. WATER AND WASTEWATER[Internet]. 2011;21(4 (76)):104-109. Available from: https://sid.ir/paper/104062/en

IEEE: Copy

ROUH ELAH NOURI, A.R. KARBASI, and H. MAHDIZADEH, “PREDICTING THE LONGITUDINAL DISPERSION COEFFICIENT IN NATURAL STREAMS USING DEVELOPED ARTIFICIAL NEURAL NETWORK MODEL,” WATER AND WASTEWATER, vol. 21, no. 4 (76), pp. 104–109, 2011, [Online]. Available: https://sid.ir/paper/104062/en

Related Journal Papers