A self-tuning ANN model for simulation and forecasting of surface flows

Omid Bozorg-Haddad (), Mahboubeh Zarezadeh-Mehrizi (), Mehri Abdi-Dehkordi (), Hugo A. Loáiciga () and Miguel A. Mariño ()
Additional contact information
Omid Bozorg-Haddad: University of Tehran
Mahboubeh Zarezadeh-Mehrizi: Tarbiat Modares University
Mehri Abdi-Dehkordi: University of Tehran
Hugo A. Loáiciga: University of California
Miguel A. Mariño: University of California

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2016, vol. 30, issue 9, No 3, 2907-2929

Abstract: Abstract Artificial neural networks (ANN) are applicable for and forecasting without the need to calculate complex nonlinear functions. This paper evaluates the effectiveness of temperature, evapotranspiration, precipitation and inflow factors, and the lag time of those factors, as variables for simulating and forecasting of runoff. The genetic algorithm (GA) is coupled with ANN to determine the optimal set of variables for streamflow forecasting. The minimization of the total mean square error (MSE) is considered as the objective function of the ANN-GA method in this paper. Our results show the effectiveness of the ANN-GA for simulating and forecasting runoff with consistent accuracy compared with using pure ANN for runoff simulation and forecasting.

Keywords: Artificial neural network; Runoff parameters; Simulation and forecasting; Effective factors; Optimization; Genetic algorithm (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://link.springer.com/10.1007/s11269-016-1301-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:30:y:2016:i:9:d:10.1007_s11269-016-1301-2

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-016-1301-2

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().