Stream Flow Modeling of River Swat Using Regression and Artificial Neural Networks (ANNs) Techniques

Suleman Daud; Khan Shahzada; M. Tufail; M. Fahad

doi:10.4028/www.scientific.net/AMR.255-260.679

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 255-260Stream Flow Modeling of River Swat Using...

Stream Flow Modeling of River Swat Using Regression and Artificial Neural Networks (ANNs) Techniques

Abstract:

This paper presents the utility of Artificial Neural Networks and Regression analysis for the stream flow modeling in Swat River at five discharge gauge station. As an appropriate intelligent model is identified, Artificial Neural Networks (ANNs) is evaluated by comparing it to regression analysis and the available field data. ANNs namely feed forward back propagation neural network (FFBPNN) and regression analysis are introduced and implemented. The research study successfully compared the performance of the ANN and regression models that validated the utility of the two modeling techniques as effective applications to stream flow forecasting.

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Periodical:

Advanced Materials Research (Volumes 255-260)

Pages:

679-683

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.255-260.679

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Online since:

May 2011

Authors:

Suleman Daud, Khan Shahzada, M. Tufail, M. Fahad

Keywords:

Artificial Neural Network (ANN), Floods, Forecasting, Regression

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References

[1] WAPDA, 1996, Water and Power Development Authority Feasibility study on the Development of Munda Dam Multipurpose Project, Directorate of Works and Investigations (WAPDA) Lahore (1996).

Google Scholar

[2] AGES, BAK & ELECKTRA Report, 2008, Feasibility Study For Construction of Bund on The Either Side of Swat River To Prevent Flooding of Habitats In The Vicinity (President Directive No. 218), Final Feasibility Report Volume-I. December 2008. Joint venture of Ages, Bak and Electra Consultants.

Google Scholar

[3] WAPDA, 1970, Water and Power Development Authority Preliminary Feasibility of Munda Dam Project, Directorate of Works and Investigations (WAPDA) Lahore (1970).

Google Scholar

[4] American Society of Civil Engineers (ASCE), Task Committee on Application of Artificial Neural Networks in Hydrology (2000a) Artificial Neural Networks in Hydrology I: Preliminary concept, Journal in Hydrologic Engineering. 5(2), 115-123.

DOI: 10.1061/(asce)1084-0699(2000)5:2(115)

Google Scholar