Assessing memory signal of time-series and simulation of rainfall-runoff process, using neural networks and wavelet-neural hybrid models

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.

FARZIN SAEED; MIRHASHEMI HAMID; ABBASI HAMED; MARYANAJI ZOHREH; Khosravinia Payam

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

Journal Paper

Paper Information

Journal: WATERSHED ENGINEERING AND MANAGEMENT Year:2020 | Volume:11 | Issue:4 Page(s): 1059-1074

Download Full-Text

Persian Verion

View:

463

Download:

Cites:

Information Journal Paper

Title

Assessing memory signal of time-series and simulation of rainfall-runoff process, using neural networks and wavelet-neural hybrid models

Author(s)

Keywords

Artificial neural networkQ1

Hurst exponentQ1

Khorramabad BasinQ1

Long-time memoryQ2

Von-Neumann testQ1

Abstract

In this study, long-term memory and dynamic behavior of daily flow time-series of Khorramabad River, which its basin is mountainous and has urban land use, is investigated by Hurst exponent. The Hurst exponent of runoff signal of Khorramabad River during 1991-2014 period was obtained as 0. 8. This value shows long-term memory and nonlinear, dynamic signal of this river’ s runoff. By applying neural network and wavelet transforms, the rainfall-runoff time-series of this river was simulated. In this respect, by taking the time-series of rainfall and rainfall-runoff as input to the Artificial neural network and wavelet-neural network hybrid, four models including: 1) rainfall, neural network, 2) rainfall-runoff, neural network, 3) rainfall, wavelet-neural network and 4) rainfall-runoff, wavelet-neural network were developed. In the hybrid models of wavelet-neural network, time-series of rainfall and runoff were decomposed to high-frequency and low-frequency sub-signals. Results of evaluating the accuracy and efficiency of the four models showed that the wavelet– neural network model correctly simulated the runoff behavior with the best efficiency at 99% confidence level. Comparison of the results of wavelet– neural network model to the neural network model, using Morgan-Granger-Newbold, showed significant superiority of the first model. Also, results of evaluating signal error of the four implemented models, using two tests of Von-Neumann and Buishand test, showed that there is a significant substitution point in the signal error of the neural network model and signal of rainfall-runoff model. Therefore, existence of very different monthly and periodical fluctuations in 1991-1998 and 1999-2014 in the behavior of rainfall-runoff leads to reduction of efficiency and precision coefficient of neural network model. While, in the hybrid model of wavelet-neural network, allocation of relative weight to each sub-signal, has effectively reduced the short-term, average and long-term fluctuations in modeling error.

Cites

No record.

References

No record.

Cite

APA: Copy

FARZIN, SAEED, MIRHASHEMI, HAMID, ABBASI, HAMED, MARYANAJI, ZOHREH, & Khosravinia, Payam. (2020). Assessing memory signal of time-series and simulation of rainfall-runoff process, using neural networks and wavelet-neural hybrid models. WATERSHED ENGINEERING AND MANAGEMENT, 11(4 ), 1059-1074. SID. https://sid.ir/paper/234826/en

Vancouver: Copy

FARZIN SAEED, MIRHASHEMI HAMID, ABBASI HAMED, MARYANAJI ZOHREH, Khosravinia Payam. Assessing memory signal of time-series and simulation of rainfall-runoff process, using neural networks and wavelet-neural hybrid models. WATERSHED ENGINEERING AND MANAGEMENT[Internet]. 2020;11(4 ):1059-1074. Available from: https://sid.ir/paper/234826/en

IEEE: Copy

SAEED FARZIN, HAMID MIRHASHEMI, HAMED ABBASI, ZOHREH MARYANAJI, and Payam Khosravinia, “Assessing memory signal of time-series and simulation of rainfall-runoff process, using neural networks and wavelet-neural hybrid models,” WATERSHED ENGINEERING AND MANAGEMENT, vol. 11, no. 4 , pp. 1059–1074, 2020, [Online]. Available: https://sid.ir/paper/234826/en

Related Journal Papers