ESTIMATING LONGITUDINAL DISPERSION COEFFICIENT OF POLLUTANTS USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

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.

RIAHI MODVAR H.; AYOUBZADEH SEYED ALI

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

Journal Paper

Paper Information

Journal: WATER AND WASTEWATER Year:2008 | Volume:19 | Issue:3 (67) Page(s): 34-46

Download Full-Text

Persian Verion

View:

1,326

Download:

Cites:

Information Journal Paper

Title

ESTIMATING LONGITUDINAL DISPERSION COEFFICIENT OF POLLUTANTS USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

Author(s)

RIAHI MODVAR H. | AYOUBZADEH SEYED ALI | Issue Writer Certificate

Keywords

POLLUTANT TRANSFERQ4

ADAPTIVE NEURO FUZZY-INFERENCE SYSTEMQ4

LONGITUDINAL DISPERSION COEFFICIENTQ3

EMPIRICAL EQUATIONSQ3

Abstract

Longitudinal dispersion coefficient in rivers and natural streams is usually estimated by simple inaccurate empirical relations because of the complexity of the phenomenon. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is used to develop a new flexible tool for predicting the LONGITUDINAL DISPERSION COEFFICIENT. The system has the ability to understand and realize the phenomenon without the need for mathematical governing equations. The training and testing of this new model are accomplished using a set of available published filed data. Several statistical and graphical criteria are used to check the accuracy of the model. The dispersion coefficient values predicted by the ANFIS model compares satisfactorily with the measured data. The predicted values are also compared with those predicted by existing EMPIRICAL EQUATIONS reported in the literature to find that the ANFIS model with R2=0.99 and RMSE=15.18 in training stage and R2=0.91 and RMSE=187.8 in testing stage is superior in predicting the dispersion coefficient to the most accurate empirical equation with R2=0.48 and RMSE=295.7. The proposed methodology is a new approach to estimating dispersion coefficient in streams and can be combined with mathematical models of POLLUTANT TRANSFER or real-time updating of these models.

Cites

References

Cite

APA: Copy

RIAHI MODVAR, H., & AYOUBZADEH, SEYED ALI. (2008). ESTIMATING LONGITUDINAL DISPERSION COEFFICIENT OF POLLUTANTS USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM. WATER AND WASTEWATER, 19(3 (67)), 34-46. SID. https://sid.ir/paper/103736/en

Vancouver: Copy

RIAHI MODVAR H., AYOUBZADEH SEYED ALI. ESTIMATING LONGITUDINAL DISPERSION COEFFICIENT OF POLLUTANTS USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM. WATER AND WASTEWATER[Internet]. 2008;19(3 (67)):34-46. Available from: https://sid.ir/paper/103736/en

IEEE: Copy

H. RIAHI MODVAR, and SEYED ALI AYOUBZADEH, “ESTIMATING LONGITUDINAL DISPERSION COEFFICIENT OF POLLUTANTS USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM,” WATER AND WASTEWATER, vol. 19, no. 3 (67), pp. 34–46, 2008, [Online]. Available: https://sid.ir/paper/103736/en

Related Journal Papers